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myUdagram | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util my elastic beanstalk endpoint http myudagram dev us east 1 elasticbeanstalk com filteredimage image url https images unsplash com photo 1453728013993 6d66e9c9123a ixlib rb 1 2 1 auto format deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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InformationTechnology | informationtechnology information technology | server |
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db-capstone-project | repository for the coursera meta database engineering professional certificate capstone project | server |
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Package-Transportation-System | package transportation system system for package transportation project for database management tools course school of electrical engineering 2021 information engineering model png technologies used java 13 jdbc mssql in documentation folder is description of all implemented interfaces | server |
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define_ansible_play_book | define ansible play book an iac to display the use of ansible to automate cloud engineering | cloud |
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tf-jam | tensor jam shooting hoops with machine learning coming soon | ai |
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chat-app | react chat application live site https react chat page netlify app react chat application client image chat app png client client folder is utilizing create react ap and socket io client you must use yarn start to load the application react chat application join page client image join png server the server is powered by nodejs and express to run the server you will need to use npm start for fast real time chat messages the chat application is running under socket io server has been deployed under heroku thank you there are more features to be added so stay tuned please feel free to reach out for any questions here is example of a good commit message for dev to | react reactjs socket-io express nodejs chat-application | front_end |
Computer-Vision-Basics-with-Python-Keras-and-OpenCV | tutorial computer vision and machine learning with python keras and opencv includes a demonstration of concepts with gesture recognition this was created as part of an educational for the western founders network https foundersnetwork ca computer vision and machine learning educational session demo the final demo can be seen here https www youtube com watch v ijv11ogtnt8 and below a href https imgflip com gif 22n3o6 img src https i imgflip com 22n3o6 gif a contents notebook ipynb https github com jrobchin computer vision basics with python keras and opencv blob master notebook ipynb contains a full tutorial of basic computer vision and machine learning concepts including what computers see image filters and functions blurring dilating erosion canny edge detectors thresholding background subtraction techniques using a background image to find differences using motion based background subtraction algorithms contours finding and sorting contours tracking deep neural networks deep convolutional neural networks demo project gesture recognition extracting the subject tracking the hand collecting data building the neural network preparing data for training training the network plotting model history note please check the issues https github com jrobchin computer vision basics with python keras and opencv issues on this repo if you re having problems with the notebook installation instructions means run this in terminal command prompt do not type windows install anaconda https www anaconda com download or miniconda https conda io miniconda html to save hard drive space start an anaconda prompt search anaconda in the start menu mac linux manually installing packages install anaconda https www anaconda com download or miniconda https conda io miniconda html to save hard drive space mac for miniconda open terminal and navigate to the directory you downloaded miniconda3 latest macosx x86 64 sh to and run bash miniconda3 latest macosx x86 64 sh for anaconda double click the anaconda3 5 0 1 macosx x86 64 pkg file you downloaded linux for miniconda open a terminal and navigate to the directory you downloaded miniconda3 latest linux x86 64 sh to and run bash miniconda3 latest linux x86 64 sh for anaconda open a terminal and navigate to the directory you downloaded anaconda3 5 0 1 linux x86 64 sh to and run bash anaconda3 5 0 1 linux x86 64 sh all platforms create and activate a python 3 5 conda environment called cv conda create n cv python 3 5 source activate cv install numpy http www numpy org conda install numpy install matplotlib https matplotlib org conda install matplotlib install keras https keras io conda install keras this should also install tensorflow install h5py http www h5py org conda install h5py install jupyter notebook http jupyter org conda install jupyter notebook install ipython https ipython org conda install ipython install opencv3 https opencv org conda install c conda forge opencv if the import cv2 does not work with this install try instead conda install c https conda anaconda org menpo opencv3 | ai |
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substrate-contracts-node | substrate contracts node open in github codespaces https github com codespaces badge svg https codespaces new paritytech substrate contracts node this repository contains substrate s node template https github com paritytech substrate tree master bin node template configured to include substrate s pallet contracts https github com paritytech substrate tree master frame contracts a smart contract module this repository contains a couple of modifications that make it unsuitable for a production deployment but a great fit for development and testing the unstable features of the pallet contracts https github com paritytech substrate tree master frame contracts are enabled by default see the runtime cargo toml https github com paritytech substrate contracts node blob main runtime cargo toml the consensus algorithm has been switched to manual seal in 42 https github com paritytech substrate contracts node pull 42 hereby blocks are authored immediately at every transaction so there is none of the typical six seconds block time associated with grandpa or aura if no cli arguments are passed the node is started in development mode by default a custom logging filter is applied by default that hides block production noise and prints the contracts debug buffer to the console with each start of the node process the chain starts from genesis so no chain state is retained all contracts will be lost if you want to retain chain state you have to supply a base path for pallet contracts config we increased the allowed contract sizes this avoids running into codetoolarge when uploading contracts during development see the comment in runtime src lib rs https github com paritytech substrate contracts node blob main runtime src lib rs for more details if you are looking for a node suitable for production see these configurations substrate node template https github com paritytech substrate tree master bin node template substrate cumulus parachain template https github com paritytech cumulus tree master parachain template contracts parachain configuration for rococo https github com paritytech cumulus tree master parachains runtimes contracts contracts rococo installation download binary the easiest way is to download a binary release from our releases page https github com paritytech substrate contracts node releases and just execute substrate contracts node build locally follow the official installation steps https docs substrate io install to set up all substrate prerequisites afterwards you can install this node via bash cargo install contracts node usage to run a local dev node execute bash substrate contracts node a new chain in temporary directory will be created each time the command is executed this is the default for this node if you want to persist chain state across runs you need to specify a directory with base path see our faq for more details how do i print something to the console from the runtime https paritytech github io ink docs faq how do i print something to the console from the runtime connect with frontend once the node template is running locally you can connect to it with frontends like contracts ui https contracts ui substrate io rpc ws 127 0 0 1 9944 or polkadot js apps https polkadot js org apps explorer rpc ws localhost 9944 and interact with your chain how to upgrade to new polkadot release note now that this repo has upgraded to using dependencies from crates io this section needs to be updated to reflect the new process once the first release of the crates from the new polkadot sdk mono repo happens check substrate s node template https github com paritytech substrate commits master bin node template for new commits between the new polkadot release branch and the one this repository is currently synced with the current branch is mentioned in this readme apply each commit that happened in this node template folder since the last sync check commits for pallet contracts https github com paritytech substrate tree master frame contracts since the last time someone synchronized this repository with substrate in order to not miss any important changes execute diener update s branch my polkadot release branch manually upgrade the pallet assets chain extension dependency to the latest release branch and then cargo update p pallet contracts for this repository the specific crate which is mentioned here is actually not important since substrate uses git references for its substrate dependencies it means that once one package is updated all are increment the minor version number in node cargo toml and runtime cargo toml execute cargo run release if successful it should produce blocks and a new up to date cargo lock will be created update this readme with the hash of the substrate master commit with which you synchronized the hash appears two times in this readme create a pr with the changes have it reviewed and merged replace xx in this command with your incremented version number and execute it git checkout main git pull git tag v0 xx 0 git push origin v0 xx 0 this will push a new tag with the version number to this repository we have set this repository up in a way that tags la vx x x trigger a ci run that creates a github draft release you can observe ci runs on gitlab https gitlab parity io parity mirrors substrate contracts node pipelines this draft release will contain a binary for linux and mac and appear under releases https github com paritytech substrate contracts node releases add a description in the style of synchronized with polkadot v1 0 0 https github com paritytech substrate tree polkadot v1 0 0 branch and publish it | blockchain substrate smart-contracts | blockchain |
llmrisks.github.io | llmrisks github io website for uva seminar on risks and benefits of generative ai and large language models | ai |
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create-near-app | create near app gitpod ready to code https img shields io badge gitpod ready to code blue logo gitpod https gitpod io https github com nearprotocol create near app quickly build apps backed by the near https near org blockchain prerequisites make sure you have a current version of node js https nodejs org en about releases installed we are targeting versions 16 read about other prerequisites https docs near org develop prerequisites in our docs getting started to create a new near project run this and follow interactive prompts npx create near app if you ve previously installed create near app globally via npm install g create near app please uninstall the package using npm uninstall g create near app to ensure that npx always uses the latest version follow the instructions in the readme md in the project you just created you can create contracts written in javascript https docs near org develop quickstart guide rust https docs near org develop prerequisites assemblyscript you can create a frontend template in react https reactjs org vanilla javascript for testing we use a sandboxed environment of near called workspaces you can write the tests in javascript or rust using cli arguments to run create near app this cli supports arguments to skip interactive prompts shell npx create near app project name contract js rust assemblyscript frontend vanilla react none tests js rust install use install to automatically install dependencies from all package json files when using arguments all arguments are required except for install getting help check out our documentation https docs near org or chat with us on discord http near chat we d love to hear from you contributing to create near app to make changes to create near app itself clone the repository windows users use git clone c core symlinks true https stackoverflow com a 42137273 249801 in your terminal enter one of the folders inside templates such as templates vanilla now you can run npm install to install dependencies and npm run dev to run the local development server just like you can in a new app created with create near app about commit messages create near app uses semantic versioning and auto generates nice release notes a changelog all based off of the commits we do this by enforcing conventional commits https www conventionalcommits org en v1 0 0 in general the pattern mostly looks like this type scope subject scope is optional multiple scopes are supported current delimiter options and real world examples can look like this chore run tests with github actions fix server send cors headers feat blog add comment section if your change should show up in release notes as a feature use feat if it should show up as a fix use fix otherwise you probably want refactor or chore more info https github com conventional changelog commitlint what is commitlint deploy create near app if you want to deploy a new version you will need two prerequisites 1 get publish access to the npm package https www npmjs com package near api js 2 get write access to the github repository https github com near near api js 3 obtain a personal access token https gitlab com profile personal access tokens it only needs the repo scope 4 make sure the token is available as an environment variable https github com release it release it blob master docs environment variables md called github token then run one script npm run release or just release it license this repository is distributed under the terms of both the mit license and the apache license version 2 0 see license license and license apache license apache for details | blockchain webassembly assemblyscript react smartcontracts rust vue angular | blockchain |
frontend-developer-roadmap | front end developer roadmap in 2022 this repository aims to collect the most important concepts of front end next i will build a professional front end knowledge map to help you master front end development front end developer roadmap react vue ecosystem high quality coding guide data structures algorithms guide this list will continue to update front end developer roadmap front end developer roadmap images frontenddeveloperroadmap svg recommended books professional javascript for web developers 4th edition react vue ecosystem react vue ecosystem images reactvueecosystem svg recommended project bestofjs https bestofjs org high quality coding guide codingguide images highqualitycodingguide svg recommended books clean code a handbook of agile software craftsmanship a philosophy of software design refactoring improving the design of existing code 2nd edition data structures algorithms guide data structures algorithms images datastructuresalgorithmsguide svg recommended books introduction to algorithms 3rd edition algorithms 4th edition contribution if you think that these can be improved in anyway please do suggest open pull request with improvements discuss ideas in issues support please star the repository to show your support share it on twitter or other places license license cc by nc nd 3 0 https img shields io badge license cc 20by nc nd 203 0 lightgrey svg https creativecommons org licenses by nc nd 3 0 | frontend front-end roadmap fullstack | front_end |
Good-Papers | some good papers i like basic background gradient regarding to algebra todo list gaussian cheatsheet kronecker factored approximations todo list asynchronous stochastic gradient descent topics with detailed notes exponentiated gradient eg expectation propagation gaussian processes gaussian processes with notes papers with detailed notes simple and accurate dependency parsing using bidirectional lstm feature representations https arxiv org abs 1603 04351 neutral relation extraction with selective attention over instances http www aclweb org anthology p16 1200 distant supervision for relation extraction via piecewise convolutional neural networks http www emnlp2015 org proceedings emnlp pdf emnlp203 pdf modeling relation paths for representation learning of knowledge bases http www aclweb org anthology d15 1082 cross sentence n ary relation extraction with graph lstms https arxiv org abs 1708 03743 six challenges for neural machine translation http www aclweb org anthology w w17 w17 3204 pdf learning to generate reviews and discovering sentiment https arxiv org pdf 1704 01444 pdf black box variational inference https arxiv org pdf 1401 0118 pdf stochastic variational deep kernel learning i e deep kernel learning for multi task classification https arxiv org pdf 1611 00336 pdf the human kernel http papers nips cc paper 5765 the human kernel additive gaussian processes http hannes nickisch org papers conferences duvenaud11gpadditive pdf kernel interpolation for scalable structured gaussian processes kiss gp http proceedings mlr press v37 wilson15 pdf gaussian process kernels for pattern discovery and extrapolation https arxiv org abs 1302 4245 adversarial examples uncertainty and transfer testing robustness in gaussian process hybrid deep networks https arxiv org pdf 1707 02476 pdf gaussian processes for classification chapter 3 of the book gaussian processes for machine learning http www gaussianprocess org gpml chapters rw3 pdf sparse gaussian processes using pseudo inputs https papers nips cc paper 2857 sparse gaussian processes using pseudo inputs multi task sequence to sequence learning https arxiv org abs 1511 06114 zero resource translation with multi lingual neural machine translation https arxiv org pdf 1606 04164 pdf multi way multilingual neural machine translation with a shared attention mechanism http www aclweb org anthology n16 1101 differentiated softmax http www aclweb org anthology p16 1186 noise contrastive estimation a new estimation principle for unnormalized statistical models http proceedings mlr press v9 gutmann10a gutmann10a pdf hierarchical probabilistic neural network language model http www iro umontreal ca lisa pointeurs hierarchical nnlm aistats05 pdf variational inference with normalizing flows https arxiv org abs 1505 05770 made masked autoencoder for distribution estimation https arxiv org abs 1502 03509 improving variational inference with inverse autoregressive flow https arxiv org abs 1606 04934 density estimation using real nvp https arxiv org abs 1605 08803 wasserstein gan https arxiv org abs 1701 07875 imagenet classification with deep convolutional neural networks https papers nips cc paper 4824 imagenet classification with deep convolutional neural networks fully convolutional networks for image segmentation https people eecs berkeley edu jonlong long shelhamer fcn pdf convolutional sequence to sequence learning https arxiv org pdf 1705 03122 pdf wavenet a generative model for raw audio https arxiv org abs 1609 03499 a deep reinforced model for abstractive summarization https arxiv org pdf 1705 04304 pdf attention is all you need https arxiv org abs 1706 03762 depthwise separable convolutions for neural machine translation https arxiv org abs 1706 03059 outrageously large neural networks the sparsely gated mixture of experts layer https openreview net pdf id b1ckmdqlg one model to learn them all https arxiv org abs 1706 05137 incorporating copying mechanism in sequence to sequence learning http www aclweb org anthology p16 1154 soundnet learning representations from unlabeled video https arxiv org abs 1610 09001 improving neural language models with a continuous cache https openreview net pdf id b184e5qee a clockwork rnn http proceedings mlr press v32 koutnik14 pdf variable computation in recurrent neural networks https arxiv org abs 1611 06188 probabilistic matrix factorization https www cs toronto edu amnih papers pmf pdf papers with quick notes neural architectures for named entity recognition https arxiv org pdf 1603 01360 pdf a good paper shows how to build a state of the art ner without using dictionary i e gazertteer or any external labeled data the only source is training and unlabeled data for training word embeddings they key lies in a simple bilstm that uses to learn feature representation of words automatically and a crf on top of the lstm to score the output here putting crf on top of the lstm aims to model that fact that we can impose several hard constraints e g i per cannot follow b loc this thing is very hard or not obvious to do with neural network i think it should be noted that the embedding of a word in context computed with a bidirectional lstm is with character based level this turns out to be very helpful to ner grammar as a foreign language https arxiv org pdf 1412 7449 pdf this paper shows how to apply sequence to sequence with attention to syntactic constituency parsing to do that they first linearize the parse tree and this can be done by following a depth first traversal order the result is very competitive with this simple model enriching word vectors with subword information https arxiv org pdf 1607 04606 pdf an interesting paper which shows that having a distance vector representation of words ignores the internal structure of words which is an important limitation for morphologically rich languages such as turkish or finnish they propose a subword model for word embedding in which a word is represented by a sequence of n grams and the word itself for instance let us consider the word where it will be represented by a sequence of 3 grams as wh whe her ere re where suppose that we are given such a sequence g of n grams and each of the n grams is represented by a specific vector representation z the scoring function between the word and its context c is computed as sum all n grams g z g v c this simple model albeit being slightly adhoc allows sharing the representations across words thus allowing to learn reliable representation for rare words compositional learning of embeddings for relation paths in knowledge bases and text https www microsoft com en us research wp content uploads 2016 06 acl2016relationpaths 1 pdf modeling relation paths have shown to offer significant gains in embedding models for knowledge base completion the key in the doing so lies in enumerating all possible paths previous work did some approximation such as random walk or paths that have a certain that is greater than a certain threshold should be involved this paper shows that we can actually do dynamic programming to effciently incoporate all relation paths the technique however is slightly complicated to understand so i just put a note here as a mark that enumerating possible paths is indeed possible to do efficiently i need further readings to understand the method better though traversing knowledge graphs in vector space https arxiv org pdf 1506 01094 pdf a knowledge graph such as freebase consists of a set of entities and their corresponding binary relations edges the paper focuses on composing these relations to form path queries in the knowledge graph path queries can be used to pose more interesting compositional questions like where are tad lincoln s parents located where we already have the entries tad lincoln lincoln dad relation and lincon located in the knowledge graph this paper does so by proposing a simple objective function to train a knowledge graph completition model j theta sum all paths with different lengths from a certain source and target entities s i t i sum all neighbors t i of target entities t i 1 margin score s i to t i score s i to t i where a implies 0 if a 0 and a if a 0 doing so surprisingly improves state of the art performance in knowledge graph completition training a model with such an objective function needs a curriculum strategy they trained on path of queries of path length 1 in the first place and then keep training with longer path of queiries distant supervision for relation extraction beyond the sentence boundary http www aclweb org anthology e17 1110 this is an interesting paper that shows how to do relation extraction using distant supervision but beyond the sentence boundary this is a setting that has not been tried before and the curiousity is of course about whether it is possible to do so the key idea is to adopt a document level graph representation that augments conventional intra sentential dependencies with new dependencies introduced for adjacent sentences and discourse relations we might ask what kind of arcs we should add to connect sentences a simple but straightforward strategy is to add an edge between the dependency roots of adjacent sentences they also investigate the impact of coreference and discourse parsing for this purpose it should be noted that as we augment this graph with new arcs the number of possible entities should grow the authors propose a nwe strategy in candidate selection which what they called minimal span candidates also the number of possible paths between entities grow the authors show that having top n shortest paths can help us restrict the candidates finally there are a bunch of useful features dependency paths have been established as a particularly effective source for relation extraction features together with dependency paths lexical item lemma part of speech tag can be also used results show that compared to extraction within single sentences cross sentence extraction attains a similar accuracy even though the recall for the latter is much higher adding more paths other than the shortest one led to a substantial improvement in accuracy adding discourse relations on the other hand consistently led to a small drop in performance while the technique in the paper is not that novel i like the idea of going beyond sentence boundary and i like the outcome of doing so distant supervision for relation extraction without labeled data https web stanford edu jurafsky mintz pdf this is a classic paper that shows that we can learn relation extraction using freebase any other knowledge base the key assumption is that given two entities that appear in the same sentences that appear in a relation in kb as well we can assume that they express the relation in someway therefore we can extract features related to the the pair of entities such as specific words between and surrounding the two entities in the sentence in which they appear the part of speech tags of these words a window of k words to the left of the entity and their part of speech tags a dependency path between each pair of entities and so on results show that the distant supervision algorithm is able to extract high precision patterns for a reasonably large number of relations i like the idea but i don t like the term distant supervision since it makes me really confusing feature hashing for large scale multitask learning http alex smola org papers 2009 weinbergeretal09 pdf this is a classic paper regarding to feature hashing given million of sparse features representing by words it would be non trivial to convert them into numerical numbers as this increases the dimensionality another problem is dealing with unknown words the paper shows that we should do hashing the words in the first place and then use the value to indicate the index of words collions may happen for sure but they show that for sparse features it would not be a problem at all also they show that we should have two kinds of hash functions one that hash words into a number and the another one that hash words into a binary of 1 and 1 by doing so it helps cancel out the collion once it happens but i don t really understand why finally the number of bits would be normally around 22 24 for the hashing but my experience is that sometimes we can need a lot more bits in overall this is a classic technique to know and use in practice i highly recommend to practice it note that from wikipedia https en wikipedia org wiki feature hashing the hashing trick isn t limited to text classification and similar tasks at the document level but can be applied to any problem that involves large perhaps unbounded numbers of features a review of relational machine learning for knowledge graphs https arxiv org pdf 1503 00759 pdf an exceptional review of relational machine learning methods for knowleged graphs the paper presents two main lines of methods that aims to predict new fact in a graph latent feature models mainly bilinear model multi layer perceptron and neural tensor networks and graph feature models the paper also presents methods that combine those two approaches the paper also describes how to train the models in general using penalized maximum likelihood training with log loss function or squared loss using squared loss function is particularly efficient in combination with a closed world assumption due to its efficiency in training it also describes how to generative negative examples which is important since knowledge graph often contains only positive training examples it shows an interesting method of training the model where the negative data is not actually negative i e using pairwise loss function with margin based ranking loss function poincar embeddings for learning hierarchical representations https papers nips cc paper 7213 poincare embeddings for learning hierarchical representations pdf this paper tries to have a better embedding models for learning hierarchical representations the goal in such an embedding is to capture not only similarity but also hierarchy similarity in the sense that placing connected nodes close to each other and unconnected nodes far from each other and hierarchy in the sense that placing nodes lower in the hierarchy farther from the origin and nodes high in the hierachy close to the origin doing so can be possible with euclidean geometry but it is difficult and requires more dimensionality space for the embedding the key to the solution is relying on hyperbolic space which is found useful in learning large network training the model should be as straightforward as training word2vec nevertheless the distance is different which is computed based on the hyperbolic space i don t really get the details but it is good o know such an interesting study learning to optimize https arxiv org abs 1606 01885 this paper explores automating algorithm design and present a method to learn an optimization method the paper uses reinforcement learning to train their own model with a note that simple supervised learning does not fit to the task since the data is not i i d the authors also mention they use guided policy search instead of simple policy gradient due to its difficulty in training policy gradient high variance overall this is a very good paper to know and there are a lot of things new to me a good reference to the paper can also be found here http bair berkeley edu blog 2017 09 12 learning to optimize with rl deep learning without poor local minima http www mit edu kawaguch publications kawaguchi nips16 pdf this paper is a good theoretical reference that shows that for deep linear neural networks every local minimum is a global minimum but for deep non linear neural networks it is also the same given that there are some unrealistic assumptions the paper is far from applicable to non linear neural networks but it is a nice progress to push forwards a better understanding of training deep neural networks identifying and attacking the saddle point problem in high dimensional non convex optimization https ganguli gang stanford edu pdf 14 saddlepoint nips pdf it is often thought that a main source of difficulty for gradient descent method to find the global minimum is the proliferation of local minima with much higher error than the global minimum actually recent results suggest this is unlikely the case and this paper is one of the studies that raise this issue specifically the paper claims that the ratio of the number of saddle points to local minima increases exponentially with the dimensionality n based on results from statistical physics random matrix theory neural network theory and empirical evidence that a deeper and more profound difficulty originates from the proliferation of saddle points not local minima especially in high dimensional problems of practical interest the authors also propose algorithms to address the problem escape saddle points but admittedly it is too technical for me to understand those the loss surfaces of multilayer networks https arxiv org pdf 1412 0233 pdf the paper provides some empirical theoretical results that strike me for large nns critical points found there are local minima of high quality measured by the test error but it is unlikely the case with small nns specificially a major difference between large and small size networks is that for the latter poor quality local minima have nonzero probability of being recovered the curse of highly variable functions for local kernel machines http nicolas le roux name publications bengio06 curse pdf the paper argues that non parametric learning is not a right approach to address high dimensional problem specificially non parametric learning prefers solutions f such that when data is similar the output would be similar as well this would not work well in high dimensional space you simply cannot just average values in a neighbour to get a meaningful value for an input we can of course change hyper parameters to define the smoothness but let s say if we have a complex function that has a million ups and downs the paper shows that we need at least a half of a million of data to learn that function this is problematic because in high dimension space the number of ups and downs is exponential while i don t really have time to understand the mathematics behind the paper i think this paper is important it covers a very broad class of algorithms kernel machine algorithms unsupervised learning algorithms laplacian eigenmaps spectral clustering semi supervised learning algorithms neural optimizer search with reinforcement learning https arxiv org abs 1709 07417 a follow up paper on optimizer search with reinforcement learning specifically a controller in the form of a recurrent neural network is trained to generate an update equation for the optimizer a simple domain specific language for update rules is developed the model is trained with reinforcement learning using the reward signal as the accuracy on a held out dataset given a fixed number of epochs experiments show promising results with neural optimizer search overall this is a very nice paper to read it is however too slow to train a network to generate update rules less than a day using 100 cpus learning to learn by gradient descent by gradient descent https arxiv org pdf 1606 04474 pdf a neat paper to read the movement of using automatically learned features is widely successful the authors question themselves whether we should learn to learn optimization algorithms automatically instead of picking a relevant optimization algorithms sgd adam momentum this is called as meta learning the authors propose a new method that updates model parameters based on theta timestep t 1 theta timestep t g time step t gradient of model parameters with respect to time step t parameter of g at time step t the authors choose g as a recurrent neural network specifically an lstm to do so while the method is interesting and promising it has downsides that the magnitude of the values being fed into the lstm can vary widly and neural networks do not perform well when that happens also it is super slow to train an lstm network to as an optimizer finally it is hard for me to believe that we can get convergence result faster with this method i e we need to train two different networks at the same time good reference of the work can be found here https theneuralperspective com 2017 01 04 learning to learn by gradient descent by gradient descent neural architecture search with reinforcement learning https openreview net pdf id r1ue8hcxg an intriguing paper which shows that we can design a good neural network automatically based on a neural architecture search specifically we can specify the structure and connectivity of a neural network by using a configuration string we then train an rnn with reinforcement learning that rewards network wigh high accuracy on a validation set the policy gradient is computed to update the controller during training in the next iteration the controler will give higher probabilities to architectures that receive high accuracies the nice thing with neural architecture search is that it can learn good model from scratch and the methods are not limited like others in that they only search models from a fixed length space the downside of the method is that it is too expensive to train such a controler overall this is a very nice neat paper to read sgdr stochastic gradient descent with warm restarts https arxiv org abs 1608 03983 a simple and fun read the authors propose a simple warm restart technique for stochastic gradient descent i don t think i understand in detail how the warm restart mechanism works but in brief the learning rate is initialized to some value and is scheduled to decrease in each restart this found to help sgd in terms of making it faster to converge 2x 4x times while the paper is nice i don t really have an intuition why warm restarts help google vizier a service for black box optimization https research google com pubs pub46180 html the paper presents design of a black box optimization framework that produces state of the art performance the framework is an internal service that has become the de factor parameter tuning at google i am however not sure whether we can try it to do optimization for our own problem but anyway it is an interesting paper to know whodunnit crime drama as a case for natural language understanding http homepages inf ed ac uk scohen tacl17csi pdf a fun read the paper introduces the task of identifying the perpetrator in a crime series as a sequence labeling problem and shows how lstm behaves with an increment process three things i learned form the work lstm could work well if it has enough data and it works much better than crf lstm does not give a firm prediction while the human does i e when human predicts a perpetrator he she sticks with that finally lstm does not have an ability of predicting the case where there is no perpetrator at all i e the victim suicides learning bilingual word embeddings with almost no bilingual data http www aclweb org anthology p17 1042 a good work shows how to learn bilingual word embeddings with only around 25 biingual word pairs it does so with a proposed self learning approach that develops on previous work with only a minor yet crucial modification that the learning process repeats over and over again the authors show very good results with good insights of why such good results are achived i don t have background of the bilingual word embeddings task but i think the paper is definitely interesting towards decoding as continuous optimisation in neural machine translation http www aclweb org anthology d17 1014 decoding in nmt is hard regarding to 1 there is a potential limit of incorporating additional global features or constraints and 2 decoding in left to right manner does not use exploited the right context from right to left manner the paper addresses the challenge by relaxing this discrete optimisation problem into a continuous optimisation problem that is we can drop the integrality i e one hot vector constraint from the predic tion variables and allow them to have soft assignments within the probability simplex the idea is bold cool and the authors are the first ones who implement such a thing a lot of work need to be done to make the model work including model initialization learning rate etc while the work is definitely good and the proposed decoding framework is novel it is unclear to me whether the relaxation is really a right way to solve these above problems backprop is not just the chain rule http timvieira github io blog post 2017 08 18 backprop is not just the chain rule this article shows a connection between backpropagation and the lagrange method while i am not a realy big fan of philosophical questions i like the interesting connection and i think it can be useful to train neural networks with some kinds of constraints a continuous relaxation of beam search for end to end training of neural sequence models https arxiv org pdf 1708 00111 pdf a good paper focuses on an interesting problem in seq2seq specifically typical cross entropy training procedures do not directly consider the behaviour of the decoding method therefore beam decoding can be suboptimal when compared with greedy decoding i e beamsize 1 the work hypothesizes that the under performance of beam search in certain scenarios can be resolved by using a better designed training objective that directly integrates beam search information equation 1 while this is ideal the new loss function is noncontinous the solution is relied on gumbel approximation the paper shows nice results with their model it is hard to say whether the technique will be implemented widely i doubt for a few things mainly because i was wondering whether hamming distance is always relevant however the paper is definitely an enjoy read non autoregressive neural machine translation https einstein ai static images pages research non autoregressive neural mt pdf and https einstein ai research non autoregressive neural machine translation i don t think i fully got all the details in the paper and i need to read it again later the core idea is building a nmt model that is not autoregressive it does so by having an additional component a fertility predictor which gives us how many target words an input word should be translated once we are given this information we can decode all input words at the same time sophisticated techniques implemented are required though the result however is not that good it is a bit hyped indeed the model obtains a magnificant improvement in terms of speed up but with a cost that translation accuracy hurts significantly the variational gaussian process https arxiv org abs 1511 06499 i don t think i fully understand the idea of using gps in variational inference but the paper really gives me a better understanding of the general picture of variational inference with hierarchical latent variables that is having a variational distribution that is also a hierarchical bayesian model is very difficult to train and the paper provides a cutting edge method to do so i recommend readers to take a look at these slides http mlg postech ac kr readinglist slides 20160822 pdf i personally learn a lot from it natural gradient http andymiller github io 2016 10 02 natural gradient bbvi html i heard about natural gradient concept for a while but i never really captured it until this paper it is a very beautiful idea and i encourage people to take a look at this tutorial to have a better understanding of the concept progressive growing of gans for improved quality stability and variation http research nvidia com sites default files pubs 2017 10 progressive growing of karras2017gan paper pdf the paper shows how to improve gans with a better training scheme recall the main problem with gan is it is very difficult to train the gradients tend to be useless if the training and generated distributions do not have substantial overlap i e too easy to tell apart among various tweaks they did to address this problem the most important one is to gradually improve the size of both generator and discriminator networks starting from a low resolution the authors add new layers that model increasingly more fine grained details as training progresses i am not really familiar with gans in practice but resulting images the improve model produces is really impressive those are perhaps the best ones to date multi task bayesian optimization https papers nips cc paper 5086 multi task bayesian optimization pdf another classic paper the authors investigate how to transfer the knowledge gained from previous optimizations to a new and related task this turns out pretty straightforward to do so under multi task gps they also investigate how to perform bayesian optimization using knowledge gained from related yet easier to optimize tasks this idea makes sense to me and it is not surprising to see how useful it is in their topic modeling case study i was however not so clear about the part of optimizing an average function over multiple tasks practical bayesian optimization of machine learning algorithms https arxiv org pdf 1206 2944 pdf a very very good paper i was aware of this work but i couldn t manage to take a look at it well it is not easy to follow if we don t have sufficient background basically given a set of hyperparameters the paper tries to address the search in a better way instead of trying all possible values as in exhaustive grid search they deploy a proxy optimization search that helps us look for promising points the results are very promising as the model is able to learn good hyperparameters much quicker than using exhaustive grid search i strongly recommend to take a look at this paper to have a taste of how gps can be used to solve a really difficult problem of hyperparameters optimization deep neural networks as gaussian processes https arxiv org abs 1711 00165 it has been long known how a gp corresponds to a neural network with single hidden layer yet it is not clear how it is with deep neural networks this work delineates the correspondence between deep neural networks and gaussian processes specificially the work provides an equation of covariance matrix that corresponds to each non linear function used in deep neural networks for relu it is easy to compute the equivalent covariance matrix but for other certain non linear functions it seems like it is not trivial to do so they provide an algorithm to compute covariance matrix which is quite difficult to follow bayesian inference also helps the model learn where it gives good prediction and where it does not figure 2 is really impressive interesting to me overall i think the paper is very interesting important what uncertainties do we need in bayesian deep learning for computer vision the paper studies the benefits of modeling uncertainty in bayesian deep learning models for vision tasks they define two types of uncertainty aleatoric uncertainty that captures noise inherent and epistemic uncertaint which accounts for uncertainty in the model they propose a bnn models that captures all these things and show how this helps visition tasks some of the techniques are too involved too me but overall i enjoyed reading the paper dropout as a bayesian approximation representing model uncertainty in deep learning https arxiv org abs 1506 02142 take home message this paper is quite thought provoking it reveals the connection between dropout training as performing approximate bayesian learning in a deep gaussian processes model it also suggests a way to get model uncertainty mcdropout which is important in practice this is because predictive mean and predictive uncertainty should provide more stable performance especially when the model is run on the wild i e the test data is compleletely different to the training data the mathematics is really involved though hogwild a lock free approach to parallelizing stochastic gradient descent http machinelearning wustl edu mlpapers paper files nips2011 0485 pdf take home message the paper ignites the trend in using asynchronous sgd instead of synchronous sgd assuming we are performing updates over very sparse parameters we can perform asynchronous sgd without any needs of locking mechanism regarding to synchronizing model parameters the mathematical proofs for the result are difficult to understand though as a side note the method does not fit to training nns because nn model parameters are not that sparse deep kernel learning https arxiv org abs 1511 02222 and stochastic variational deep kernel learning http papers nips cc paper 6425 stochastic variational deep kernel learning and learning scalable deep kernels with recurrent structure https arxiv org abs 1610 08936 take home message the studies contribute a hybridge architecture between gps and deep neural networks the combination makes sense and experiment results show promising results training the model is end to end and scalable the scalability is mainly due previous work of andrew wilson et al though not in those studies per say i found the research line very inspiring yet the papers are really technical to follow assessing approximations for gaussian process classification http papers nips cc paper 2903 assessing approximations for gaussian process classification pdf and its longer version assessing approximate inference for binary gaussian process classification http www jmlr org papers volume6 kuss05a kuss05a pdf take home message gp classification models are intractable to train there are three main choices to ease the intractability using laplace s method expectation propagation and mcmc mcmc works best but it is too expensive laplace s method is simple but the paper suggests that it is very inaccurate ep works surprisingly well sequential inference for deep gaussian process http www2 ift ulaval ca chaib publications yali aistas16 pdf and training and inference for deep gaussian processes undergrad thesis http keyonvafa com deep gaussian processes take home message deep gps are powerful models yet difficult to train inference due to computation intractability the papers address the problem by using sampling mechanisms the techniques are very straightforward the first paper totally eases computational cost by using an active set instead of the full dataset the size of active set has a quite significant impact to the performance though as a side note the performance really depends on parameter initialization the second paper the first paper really shows the benefits of having deep gp models even though a deep gp model does not work well with mnist classification the accuracy is quite low around 94 95 the first paper is really good and deserves more attention efficient softmax approximation for gpus https arxiv org abs 1609 04309 take home message providing a systematic comparison between various methods for speeding up training nlms with large vocabulary the paper also proposes an one that pretty fits to gpus their method is very technical to follow though the proposed one works best meanwhile their modification to differentiated softmax works pretty well but it is totally unclear to me how they modify d softmax strategies for training large vocabulary neural language models http www aclweb org anthology p16 1186 take home message providing a systematic comparison between various methods for speeding up training neural language models with large vocabulary hierarchical softmax works best for large dataset very surprising differentied softmax works well for small scale dataset but the speed up factor is not so high nce works very bad and self normalization works ok good notes on the paper can be also found here https github com dennybritz deeplearning papernotes blob master notes strategies for training large vocab lm md see hear and read deep aligned representations https arxiv org abs 1706 00932 the paper proposes a nice cross modal networks to approach the challenge of learning discriminative representations shared across modalities given inputs as different types image sound text the model produces a common representation shared across modalilities the common representation can bring huge benefits for instance let us assume we have pairs of images and sound from videos let us also assume we have pairs of images and text from caption datasets such a common representation can map between sound and text using images as a bridge pretty cool it is however unclear from the paper how the cross model networks are designed implemented lots of technical details are missing and it is very hard to walk through the paper bagging by design on the suboptimality of bagging https www aaai org ocs index php aaai aaai14 paper view 8406 take home message a nice study proposing a provably optimal subsampling design bagging method the proposed one outperforms the original bagging method convincingly both theoretical and experimental aspects on multiplicative integration with recurrent neural networks https arxiv org abs 1606 06630 take home message we can modify the additive integration with multiplicative integration in rnn the goal is to make transition i e gradient state over state tighter to inputs importance weighted autoencoders https arxiv org abs 1509 00519 take home message a nice paper showing that training with weighted sample always better a clear explanatio from the paper also one can tighten the bound simply by drawing more samples in the monte carlo objective adversarial autoencoders https arxiv org abs 1511 05644 take home message instead of using kl divergence as in variational autoencoders we should rather optimize js divergence this makes sense as js could better than kl in inference on large batch training for deep learning generalization gap and sharp minima https arxiv org abs 1609 04836 take home message very good paper explaing why sgd with small batch size is so useful an optimizer should aim for flat minima maxima instead of sharp minima maxima small batch size helps achieve this because there is lots of noisy gradients in training deep exponential families https arxiv org abs 1411 2581 take home message stacking multiple exponential models up to 3 layers can improve the performance inference is much harder though i personally like this work a lot semi supervised learning with deep generative models https arxiv org abs 1406 5298 take home message a classic on semi supervised learning with deep generative models using stochastic variational inference for training the model may not work as well as ladder networks yet it is classic and has broad applications exponential family embeddings https arxiv org abs 1608 00778 take home message a very cool work showing how to generalize word2vec to other very interesting models e g items in a bucket also instead of using exp as in the original model the paper shows other possibilities including posson gaussian bernoulil i personally like this work a lot hierarchical variational models https arxiv org abs 1511 02386 take home message showing how to increase the richness of q function by using a hierarchical model with global paramter the model itself is equivalent to auxiliary deep generative models https arxiv org abs 1602 05473 the marginal value of adaptive gradient methods in machine learning https arxiv org abs 1705 08292 take home message adagrad adam and other adaptive methods are awesome but if we tune sgd properly we could do much better markov chain monte carlo and variational inference bridging the gap https arxiv org abs 1410 6460 take home message the paper proposes a very nice idea how to improve mcmc using variational inference by exploiting the likelihood to see whether the chain converges and to estimate the parameter for tuning mcmc meanwhile it also can help variational inference using mcmc but how this is the point i don t really quite get categorical variational autoencoders using gumbel softmax https arxiv org abs 1611 01144 take home message how to convert discrite variable into an approximate form that fits into reparameterization tricks using gumbel softmax function context gates for neural machine translation https arxiv org abs 1608 06043 take home message the paper shows that in seq2seq we should control how a word is generated a content word should generated based on inputs while a common word should be generated based on the context of target words the paper proposes a gate network that integrate the information into seq2seq in a nice way https ai googleblog com 2018 05 advances in semantic textual similarity html some models trained on conversation data for sentence embedding as well as some models use for question answering pretty basic rationalizing neural predictions very nice paper that learn the rationale behind the prediction given input https aclweb org anthology d16 1011 | deep-learning machine-learning natural-language-processing computer-vision gaussian-processes variational-autoencoders neural-machine-translation neural-network convolutional-networks semi-supervised-learning variational-inference kernel-learning representation-learning deep-kernel-learning | ai |
johnny-five | https github com rwaldron johnny five raw main assets sgier johnny five png johnny five the javascript robotics programming framework hello please don t edit this file if you d like to make changes to the readme contents please make them in the tpl readme md file if you d like to add an example 1 add the file in eg 2 add a breadboard image in docs breadboards 3 add an entry to tpl programs json 4 generated the markdown with grunt examples artwork by mike sgier http msgierillustration com build lint test and measure coverage https github com rwaldron johnny five actions workflows npm grunt yml badge svg https github com rwaldron johnny five actions appveyor build status https ci appveyor com api projects status hmke71k7uemtnami branch main svg true https ci appveyor com project rwaldron johnny five coverage status https coveralls io repos github rwaldron johnny five badge svg branch main https coveralls io github rwaldron johnny five branch main install size https packagephobia now sh badge p johnny five https packagephobia now sh result p johnny five gitter https img shields io gitter room nwjs nw js svg https gitter im rwaldron johnny five johnny five is an open source firmata protocol based iot and robotics programming framework developed by the nodebots https twitter com nodebots community johnny five programs can be written for arduino all models electric imp beagle bone intel galileo edison linino one pinoccio pcduino3 raspberry pi particle spark core photon tessel 2 ti launchpad and more johnny five has grown from a passion project into a tool for inspiring learning and creativity for people of all ages backgrounds and from all across the world just interested in learning and building awesome things you might want to start with the official johnny five website http johnny five io if you want to find the api documentation that s right here http johnny five io api need to figure out what platform to use for a project we put that stuff here http johnny five io platform support need inspiration for your next nodebot check out the examples http johnny five io examples want to stay up to date with projects in the community check this out http johnny five io articles need nodebots community or johnny five project updates and announcements this http johnny five io news is what you re looking for johnny five does not attempt to provide all the things but instead focuses on delivering robust reality tested highly composable apis that behave consistently across all supported hardware platforms johnny five wants to be a baseline control kit for hardware projects allowing you the freedom to build grow and experiment with diverse javascript libraries of your own choice johnny five couples comfortably with popular application libraries such as express js http expressjs com and socket io http socket io fellow hardware projects like ar drone https github com felixge node ar drone aerogel https github com ceejbot aerogel and spheron https github com alchemycs spheron bluetooth game controllers like xbox controller https github com andrew node xbox controller and dualshock https github com rdepena node dualshock controller iot frameworks such as octoblu http www octoblu com and that s only a few of the many explorable possibilities check out these exciting projects node pulsesensor https www npmjs org package node pulsesensor footballbot workshop ui https www npmjs org package footballbot workshop ui nodebotui https www npmjs org package nodebotui dublin disco https www npmjs org package dublin disco node slot car bot https www npmjs org package node slot car bot servo calibrator https www npmjs org package servo calibrator node ardx https www npmjs org package node ardx nodebot workshop https www npmjs org package nodebot workshop phone home https www npmjs org package phone home purple unicorn https www npmjs org package purple unicorn webduino https www npmjs org package webduino leapduino https www npmjs org package leapduino lasercat workshop https www npmjs org package lasercat workshop simplesense https www npmjs org package simplesense five redbot https www npmjs org package five redbot robotnik https www npmjs org package robotnik the blender https www npmjs org package the blender why javascript nodebots the rise of javascript robotics http www voodootikigod com nodebots the rise of js robotics hello johnny the ubiquitous hello world program of the microcontroller and soc world is blink an led the following code demonstrates how this is done using the johnny five framework javascript const board led require johnny five const board new board board on ready create an led on pin 13 const led new led 13 blink every half second led blink 500 img src https github com rwaldron johnny five raw main assets led blink gif note node will crash if you try to run johnny five in the node repl but board instances will create their own contextual repl put your script in a file supported hardware johnny five has been tested on a variety of arduino compatible boards https github com rwaldron johnny five wiki board for non arduino based projects a number of platform specific io plugins https github com rwaldron johnny five wiki io plugins are available io plugins allow johnny five code to communicate with any non arduino based hardware in whatever language that platforms speaks documentation documentation for the johnny five api can be found here http johnny five io api and example programs here http johnny five io examples guidance need help ask a question on the nodebots community forum http forums nodebots io if you just have a quick question or are interested in ongoing design discussions join us in the johnny five gitter chat https gitter im rwaldron johnny five for step by step examples including an electronics primer check out arduino experimenter s guide for nodejs http node ardx org by annagerber https twitter com annagerber here is a list of prerequisites https github com rwaldron johnny five wiki getting started prerequisites for linux osx or windows check out the bluetooth guide https github com rwaldron johnny five wiki jy mcu bluetooth serial port module notes if you want to remotely control your robot setup and assemble arduino recommended starting kit sparkfun inventor s kit https www sparkfun com products 12001 download arduino ide http arduino cc en main software plug in your arduino or arduino compatible microcontroller via usb open the arduino ide select file examples firmata standardfirmataplus standardfirmataplus is available in firmata v2 5 0 or greater click the upload button if the upload was successful the board is now prepared and you can close the arduino ide for non arduino projects each io plugin s repo will provide its own platform specific setup instructions hey you here s johnny source code bash git clone git github com rwaldron johnny five git cd johnny five npm install npm package install the module with bash npm install johnny five example programs to get you up and running quickly we provide a variety of examples for using each johnny five component one thing we re especially excited about is the extensive collection of fritzing http fritzing org home diagrams you ll find throughout the site a huge part of doing any johnny five project is handling the actual hardware and we ve included these as part of the documentation because we realised that instructions on how to write code to control a servo are insufficient without instructions on how to connect a servo to interactively navigate the examples visit the johnny five examples http johnny five io examples page on the official website if you want to link directly to the examples in this repo you can use one of the following links there are presently 362 example programs with code and diagrams extract start examples board board basic initialization https github com rwaldron johnny five blob main docs board md board cleanup in exit event https github com rwaldron johnny five blob main docs board cleanup md board multiple in one program https github com rwaldron johnny five blob main docs board multi md board specify sampling interval https github com rwaldron johnny five blob main docs board sampling interval md board specify port https github com rwaldron johnny five blob main docs board with port md custom data properties https github com rwaldron johnny five blob main docs custom properties md pin https github com rwaldron johnny five blob main docs pin md repl https github com rwaldron johnny five blob main docs repl md led led https github com rwaldron johnny five blob main docs led md led blink https github com rwaldron johnny five blob main docs led blink md led demo sequence https github com rwaldron johnny five blob main docs led demo sequence md led fade https github com rwaldron johnny five blob main docs led fade md led fade callback https github com rwaldron johnny five blob main docs led fade callback md led fade with animation https github com rwaldron johnny five blob main docs led fade animation md led pca9685 https github com rwaldron johnny five blob main docs led pca9685 md led pulse https github com rwaldron johnny five blob main docs led pulse md led pulse with animation https github com rwaldron johnny five blob main docs led pulse animation md led slider https github com rwaldron johnny five blob main docs led slider md led tessel servo module https github com rwaldron johnny five blob main docs led tessel servo module md leds an array of leds https github com rwaldron johnny five blob main docs led array md leds controlling an array of leds https github com rwaldron johnny five blob main docs led array controller md led rgb led rgb common anode https github com rwaldron johnny five blob main docs led rgb anode md led rgb common anode pca9685 https github com rwaldron johnny five blob main docs led rgb anode pca9685 md led rgb intensity https github com rwaldron johnny five blob main docs led rgb intensity md led rainbow https github com rwaldron johnny five blob main docs led rainbow md led rainbow blinkm https github com rwaldron johnny five blob main docs led rgb blinkm md led digits matrix led digital clock https github com rwaldron johnny five blob main docs led digits clock md led digital clock dual displays https github com rwaldron johnny five blob main docs led digits clock dual md led digital clock ht16k33 https github com rwaldron johnny five blob main docs led digits clock ht16k33 md led draw matrix characters demo https github com rwaldron johnny five blob main docs led chars demo md led enumerate matrix characters symbols https github com rwaldron johnny five blob main docs led enumeratechars md led matrix https github com rwaldron johnny five blob main docs led matrix md led matrix demo https github com rwaldron johnny five blob main docs led matrix tutorial md led matrix ht16k33 https github com rwaldron johnny five blob main docs led matrix ht16k33 md led matrix ht16k33 16x8 https github com rwaldron johnny five blob main docs led matrix ht16k33 16x8 md servo servo https github com rwaldron johnny five blob main docs servo md servo continuous https github com rwaldron johnny five blob main docs servo continuous md servo drive https github com rwaldron johnny five blob main docs servo drive md servo multi turn https github com rwaldron johnny five blob main docs servo multi turn md servo pca9685 https github com rwaldron johnny five blob main docs servo pca9685 md servo prompt https github com rwaldron johnny five blob main docs servo prompt md servo slider control https github com rwaldron johnny five blob main docs servo slider md servo tessel servo module https github com rwaldron johnny five blob main docs servo tessel servo module md servos an array of servos https github com rwaldron johnny five blob main docs servo array md gps gps adafruit ultimate gps breakout https github com rwaldron johnny five blob main docs gps adafruit md gps default gps https github com rwaldron johnny five blob main docs gps md gps hardware serial https github com rwaldron johnny five blob main docs gps hardware serial md gps sparkfun gp 20u7 https github com rwaldron johnny five blob main docs gps gp 20u7 md servo animation servo animation https github com rwaldron johnny five blob main docs servo animation md servo leg animation https github com rwaldron johnny five blob main docs servo animation leg md color color evshield ev3 code https github com rwaldron johnny five blob main docs color evs ev3 md color evshield ev3 raw https github com rwaldron johnny five blob main docs color raw evs ev3 md color evshield nxt code https github com rwaldron johnny five blob main docs color evs nxt md color isl29125 https github com rwaldron johnny five blob main docs color isl29125 md motor motor https github com rwaldron johnny five blob main docs motor md motor 3 pin https github com rwaldron johnny five blob main docs motor 3 pin md motor adafruit drv8871 dc motor driver breakout https github com rwaldron johnny five blob main docs motor drv8871 md motor brake https github com rwaldron johnny five blob main docs motor brake md motor current https github com rwaldron johnny five blob main docs motor current md motor directional https github com rwaldron johnny five blob main docs motor directional md motor evshield ev3 https github com rwaldron johnny five blob main docs motor evs ev3 md motor evshield nxt https github com rwaldron johnny five blob main docs motor evs nxt md motor enable pin https github com rwaldron johnny five blob main docs motor enable md motor grove i2c motor driver https github com rwaldron johnny five blob main docs motor grove i2c md motor h bridge https github com rwaldron johnny five blob main docs motor hbridge md motor ludus https github com rwaldron johnny five blob main docs motor ludus md motor pca9685 https github com rwaldron johnny five blob main docs motor pca9685 md motor pololu vnh5019 dual motor driver breakout https github com rwaldron johnny five blob main docs motor vnh5019 md motor sparkfun dual h bridge edison block https github com rwaldron johnny five blob main docs motor sparkfun edison hbridge md motor sparkfun tb6612fng https github com rwaldron johnny five blob main docs motor tb6612fng md motor l298 breakout https github com rwaldron johnny five blob main docs motor l298 breakout md motors dual h bridge https github com rwaldron johnny five blob main docs motor hbridge dual md stepper motor stepper driver https github com rwaldron johnny five blob main docs stepper driver md stepper four wire https github com rwaldron johnny five blob main docs stepper four wire md stepper sweep https github com rwaldron johnny five blob main docs stepper sweep md esc brushless motor esc bidirectional https github com rwaldron johnny five blob main docs esc bidirectional md esc keypress controlled escs https github com rwaldron johnny five blob main docs esc keypress md esc pca9685 https github com rwaldron johnny five blob main docs esc pca9685 md button switch button https github com rwaldron johnny five blob main docs button md button bumper https github com rwaldron johnny five blob main docs button bumper md button evshield ev3 https github com rwaldron johnny five blob main docs button evs ev3 md button evshield nxt https github com rwaldron johnny five blob main docs button evs nxt md button options https github com rwaldron johnny five blob main docs button options md button pullup https github com rwaldron johnny five blob main docs button pullup md buttons collection w at42qt1070 https github com rwaldron johnny five blob main docs button collection at42qt1070 md switch https github com rwaldron johnny five blob main docs switch md switch magnetic door https github com rwaldron johnny five blob main docs switch magnetic door md switch tilt sw 200d https github com rwaldron johnny five blob main docs switch tilt sw 200d md toggle switch https github com rwaldron johnny five blob main docs toggle switch md keypad keypad 3x4 i2c nano backpack https github com rwaldron johnny five blob main docs keypad 3x4 i2c nano backpack md keypad 4x4 i2c nano backpack https github com rwaldron johnny five blob main docs keypad 4x4 i2c nano backpack md keypad vkey https github com rwaldron johnny five blob main docs keypad analog vkey md keypad waveshare ad https github com rwaldron johnny five blob main docs keypad analog ad md touchpad grove qtouch https github com rwaldron johnny five blob main docs keypad qtouch md touchpad mpr121 https github com rwaldron johnny five blob main docs keypad mpr121 md touchpad mpr121 sensitivity https github com rwaldron johnny five blob main docs keypad mpr121 sensitivity md touchpad mpr121qr2 shield https github com rwaldron johnny five blob main docs keypad mpr121qr2 shield md touchpad mpr121 keypad https github com rwaldron johnny five blob main docs keypad mpr121 keypad md touchpad mpr121 shield https github com rwaldron johnny five blob main docs keypad mpr121 shield md relay relay https github com rwaldron johnny five blob main docs relay md relay collection https github com rwaldron johnny five blob main docs relay collection md relay on analog pin https github com rwaldron johnny five blob main docs relay on analog pin md shift register shift register https github com rwaldron johnny five blob main docs shift register md shift register common anode seven segment controller https github com rwaldron johnny five blob main docs shift register seven segment anode md shift register common anode seven segments chained https github com rwaldron johnny five blob main docs shift register daisy chain anode md shift register seven segment controller https github com rwaldron johnny five blob main docs shift register seven segment md shift register seven segments chained https github com rwaldron johnny five blob main docs shift register daisy chain md infrared reflectance ir motion https github com rwaldron johnny five blob main docs ir motion md ir proximity https github com rwaldron johnny five blob main docs ir proximity md ir reflectance https github com rwaldron johnny five blob main docs ir reflect md ir reflectance array https github com rwaldron johnny five blob main docs ir reflect array md proximity proximity https github com rwaldron johnny five blob main docs proximity md proximity evshield ev3 ir https github com rwaldron johnny five blob main docs proximity evs ev3 ir md proximity evshield ev3 ir https github com rwaldron johnny five blob main docs proximity evs ev3 ir alert md proximity evshield ev3 ultrasonic https github com rwaldron johnny five blob main docs proximity evs ev3 us md proximity evshield ev3 ultrasonic https github com rwaldron johnny five blob main docs proximity evs ev3 us alert md proximity gp2y0a710k0f https github com rwaldron johnny five blob main docs proximity gp2y0a710k0f md proximity hc sr04 https github com rwaldron johnny five blob main docs proximity hcsr04 md proximity hc sr04 analog https github com rwaldron johnny five blob main docs proximity hcsr04 analog md proximity hc sr04 i2c backpack https github com rwaldron johnny five blob main docs proximity hcsr04 i2c md proximity lidar lite https github com rwaldron johnny five blob main docs proximity lidarlite md proximity mb1000 https github com rwaldron johnny five blob main docs proximity mb1000 md proximity mb1003 https github com rwaldron johnny five blob main docs proximity mb1003 md proximity mb1010 https github com rwaldron johnny five blob main docs proximity mb1010 md proximity mb1230 https github com rwaldron johnny five blob main docs proximity mb1230 md proximity srf10 https github com rwaldron johnny five blob main docs proximity srf10 md motion motion https github com rwaldron johnny five blob main docs motion md motion gp2y0a60szlf https github com rwaldron johnny five blob main docs motion gp2y0a60szlf md motion gp2y0d805z0f https github com rwaldron johnny five blob main docs motion gp2y0d805z0f md motion gp2y0d810z0f https github com rwaldron johnny five blob main docs motion gp2y0d810z0f md motion gp2y0d810z0f https github com rwaldron johnny five blob main docs motion gp2y0d815z0f md joystick joystick https github com rwaldron johnny five blob main docs joystick md joystick esplora https github com rwaldron johnny five blob main docs joystick esplora md joystick pan tilt control https github com rwaldron johnny five blob main docs joystick pantilt md joystick sparkfun shield https github com rwaldron johnny five blob main docs joystick shield md lcd grove rgb lcd color previewer https github com rwaldron johnny five blob main docs lcd rgb bgcolor previewer md lcd https github com rwaldron johnny five blob main docs lcd md lcd enumerate characters https github com rwaldron johnny five blob main docs lcd enumeratechars md lcd i2c https github com rwaldron johnny five blob main docs lcd i2c md lcd i2c pcf8574 https github com rwaldron johnny five blob main docs lcd i2c pcf8574 md lcd i2c runner https github com rwaldron johnny five blob main docs lcd i2c runner md lcd runner 16x2 https github com rwaldron johnny five blob main docs lcd runner md lcd runner 20x4 https github com rwaldron johnny five blob main docs lcd runner 20x4 md lcd tessel 2 16x2 https github com rwaldron johnny five blob main docs lcd 16x2 tessel md tessel 2 grove rgb lcd color previewer https github com rwaldron johnny five blob main docs lcd rgb bgcolor previewer tessel md tessel 2 grove rgb lcd display https github com rwaldron johnny five blob main docs lcd rgb tessel grove jhd1313m1 md compass magnetometer compass find north https github com rwaldron johnny five blob main docs magnetometer north md compass hmc5883l https github com rwaldron johnny five blob main docs compass hmc5883l md compass hmc6352 https github com rwaldron johnny five blob main docs compass hmc6352 md compass logger https github com rwaldron johnny five blob main docs magnetometer log md compass mag3110 https github com rwaldron johnny five blob main docs compass mag3110 md compass mag3110 on tessel 2 https github com rwaldron johnny five blob main docs compass mag3110 tessel md compass magnetometer https github com rwaldron johnny five blob main docs magnetometer md piezo piezo https github com rwaldron johnny five blob main docs piezo md imu multi imu bno055 https github com rwaldron johnny five blob main docs imu bno055 md imu bno055 orientation https github com rwaldron johnny five blob main docs imu bno055 orientation md imu lsm303c https github com rwaldron johnny five blob main docs imu lsm303c md imu mpu6050 https github com rwaldron johnny five blob main docs imu mpu6050 md multi bme280 https github com rwaldron johnny five blob main docs multi bme280 md multi bmp085 https github com rwaldron johnny five blob main docs multi bmp085 md multi bmp180 https github com rwaldron johnny five blob main docs multi bmp180 md multi dht11 i2c nano backpack https github com rwaldron johnny five blob main docs multi dht11 i2c nano backpack md multi dht21 i2c nano backpack https github com rwaldron johnny five blob main docs multi dht21 i2c nano backpack md multi dht22 i2c nano backpack https github com rwaldron johnny five blob main docs multi dht22 i2c nano backpack md multi hih6130 https github com rwaldron johnny five blob main docs multi hih6130 md multi htu21d https github com rwaldron johnny five blob main docs multi htu21d md multi mpl115a2 https github com rwaldron johnny five blob main docs multi mpl115a2 md multi mpl3115a2 https github com rwaldron johnny five blob main docs multi mpl3115a2 md multi ms5611 https github com rwaldron johnny five blob main docs multi ms5611 md multi sht31d https github com rwaldron johnny five blob main docs multi sht31d md multi si7020 https github com rwaldron johnny five blob main docs multi si7020 md multi si7021 https github com rwaldron johnny five blob main docs multi si7021 md multi th02 https github com rwaldron johnny five blob main docs multi th02 md sensors accelerometer https github com rwaldron johnny five blob main docs accelerometer md accelerometer adxl335 https github com rwaldron johnny five blob main docs accelerometer adxl335 md accelerometer adxl345 https github com rwaldron johnny five blob main docs accelerometer adxl345 md accelerometer lis3dh https github com rwaldron johnny five blob main docs accelerometer lis3dh md accelerometer mma7361 https github com rwaldron johnny five blob main docs accelerometer mma7361 md accelerometer mma8452 https github com rwaldron johnny five blob main docs accelerometer mma8452 md accelerometer mpu6050 https github com rwaldron johnny five blob main docs accelerometer mpu6050 md accelerometer pan tilt https github com rwaldron johnny five blob main docs accelerometer pan tilt md altimeter bmp085 https github com rwaldron johnny five blob main docs altimeter bmp085 md altimeter bmp180 https github com rwaldron johnny five blob main docs altimeter bmp180 md altimeter mpl3115a2 https github com rwaldron johnny five blob main docs altimeter mpl3115a2 md altimeter ms5611 https github com rwaldron johnny five blob main docs altimeter ms5611 md barometer bmp085 https github com rwaldron johnny five blob main docs barometer bmp085 md barometer bmp180 https github com rwaldron johnny five blob main docs barometer bmp180 md barometer mpl115a2 https github com rwaldron johnny five blob main docs barometer mpl115a2 md barometer mpl3115a2 https github com rwaldron johnny five blob main docs barometer mpl3115a2 md barometer ms5611 https github com rwaldron johnny five blob main docs barometer ms5611 md gyro https github com rwaldron johnny five blob main docs gyro md gyro analog lpr5150al https github com rwaldron johnny five blob main docs gyro lpr5150l md gyro i2c mpu6050 https github com rwaldron johnny five blob main docs gyro mpu6050 md hygrometer dht11 i2c nano backpack https github com rwaldron johnny five blob main docs hygrometer dht11 i2c nano backpack md hygrometer dht21 i2c nano backpack https github com rwaldron johnny five blob main docs hygrometer dht21 i2c nano backpack md hygrometer dht22 i2c nano backpack https github com rwaldron johnny five blob main docs hygrometer dht22 i2c nano backpack md hygrometer hih6130 https github com rwaldron johnny five blob main docs hygrometer hih6130 md hygrometer htu21d https github com rwaldron johnny five blob main docs hygrometer htu21d md hygrometer sht31d https github com rwaldron johnny five blob main docs hygrometer sht31d md hygrometer si7021 https github com rwaldron johnny five blob main docs hygrometer si7021 md hygrometer th02 https github com rwaldron johnny five blob main docs hygrometer th02 md sensor https github com rwaldron johnny five blob main docs sensor md sensor digital microwave https github com rwaldron johnny five blob main docs sensor digital microwave md sensor flex sensor https github com rwaldron johnny five blob main docs flex md sensor force sensitive resistor https github com rwaldron johnny five blob main docs sensor fsr md sensor microphone https github com rwaldron johnny five blob main docs microphone md sensor photoresistor https github com rwaldron johnny five blob main docs photoresistor md sensor potentiometer https github com rwaldron johnny five blob main docs potentiometer md sensor slide potentiometer https github com rwaldron johnny five blob main docs sensor slider md thermometer bmp085 https github com rwaldron johnny five blob main docs temperature bmp085 md thermometer bmp180 https github com rwaldron johnny five blob main docs temperature bmp180 md thermometer dht11 i2c nano backpack https github com rwaldron johnny five blob main docs temperature dht11 i2c nano backpack md thermometer dht21 i2c nano backpack https github com rwaldron johnny five blob main docs temperature dht21 i2c nano backpack md thermometer dht22 i2c nano backpack https github com rwaldron johnny five blob main docs temperature dht22 i2c nano backpack md thermometer ds18b20 https github com rwaldron johnny five blob main docs temperature ds18b20 md thermometer dual ds18b20 https github com rwaldron johnny five blob main docs temperature dual ds18b20 md thermometer hih6130 https github com rwaldron johnny five blob main docs temperature hih6130 md thermometer htu21d https github com rwaldron johnny five blob main docs temperature htu21d md thermometer lm335 https github com rwaldron johnny five blob main docs temperature lm335 md thermometer lm35 https github com rwaldron johnny five blob main docs temperature lm35 md thermometer max31850 https github com rwaldron johnny five blob main docs temperature max31850k md thermometer mcp9808 https github com rwaldron johnny five blob main docs temperature mcp9808 md thermometer mpl115a2 https github com rwaldron johnny five blob main docs temperature mpl115a2 md thermometer mpl3115a2 https github com rwaldron johnny five blob main docs temperature mpl3115a2 md thermometer mpu6050 https github com rwaldron johnny five blob main docs temperature mpu6050 md thermometer ms5611 https github com rwaldron johnny five blob main docs temperature ms5611 md thermometer sht31d https github com rwaldron johnny five blob main docs temperature sht31d md thermometer si7020 https github com rwaldron johnny five blob main docs temperature si7020 md thermometer si7021 https github com rwaldron johnny five blob main docs temperature si7021 md thermometer th02 https github com rwaldron johnny five blob main docs temperature th02 md thermometer tmp102 https github com rwaldron johnny five blob main docs temperature tmp102 md thermometer tmp36 https github com rwaldron johnny five blob main docs temperature tmp36 md expander expander 74hc595 https github com rwaldron johnny five blob main docs expander 74hc595 md expander cd74hc4067 16 channel analog input breakout https github com rwaldron johnny five blob main docs expander cd74hc4067 nano backpack md expander lis3dh https github com rwaldron johnny five blob main docs expander lis3dh md expander mcp23008 https github com rwaldron johnny five blob main docs expander mcp23008 md expander mcp23017 https github com rwaldron johnny five blob main docs expander mcp23017 md expander muxshield2 analog sensors https github com rwaldron johnny five blob main docs expander muxshield2 analog read md expander muxshield2 digital input and output https github com rwaldron johnny five blob main docs expander muxshield2 mixed md expander pca9685 https github com rwaldron johnny five blob main docs expander pca9685 md expander pcf8574 https github com rwaldron johnny five blob main docs expander pcf8574 md expander pcf8575 https github com rwaldron johnny five blob main docs expander pcf8575 md expander pcf8591 https github com rwaldron johnny five blob main docs expander pcf8591 md photon weather shield photon weather shield moisture https github com rwaldron johnny five blob main docs sensor photon weather shield moisture md lego evshield button evshield ev3 https github com rwaldron johnny five blob main docs button evs ev3 md button evshield nxt https github com rwaldron johnny five blob main docs button evs nxt md color evshield ev3 code https github com rwaldron johnny five blob main docs color evs ev3 md color evshield ev3 raw https github com rwaldron johnny five blob main docs color raw evs ev3 md color evshield nxt code https github com rwaldron johnny five blob main docs color evs nxt md light bh1750 https github com rwaldron johnny five blob main docs light ambient bh1750 md light evshield ev3 ambient https github com rwaldron johnny five blob main docs light ambient evs ev3 md light evshield ev3 reflected https github com rwaldron johnny five blob main docs light reflected evs ev3 md light evshield nxt ambient https github com rwaldron johnny five blob main docs light ambient evs nxt md light evshield nxt reflected https github com rwaldron johnny five blob main docs light reflected evs nxt md light tsl2561 https github com rwaldron johnny five blob main docs light ambient tsl2561 md motor evshield ev3 https github com rwaldron johnny five blob main docs motor evs ev3 md motor evshield nxt https github com rwaldron johnny five blob main docs motor evs nxt md proximity evshield ev3 ir https github com rwaldron johnny five blob main docs proximity evs ev3 ir alert md proximity evshield ev3 ultrasonic https github com rwaldron johnny five blob main docs proximity evs ev3 us alert md intel edison grove iot kit intel edison grove accelerometer adxl345 https github com rwaldron johnny five blob main docs grove accelerometer adxl345 edison md intel edison grove accelerometer mma7660 https github com rwaldron johnny five blob main docs grove accelerometer mma7660 edison md intel edison grove air quality sensor https github com rwaldron johnny five blob main docs grove gas tp401 edison md intel edison grove barometer bmp180 https github com rwaldron johnny five blob main docs grove barometer edison md intel edison grove button https github com rwaldron johnny five blob main docs grove button edison md intel edison grove compass hmc588l https github com rwaldron johnny five blob main docs grove compass edison md intel edison grove flame sensor https github com rwaldron johnny five blob main docs grove flame sensor edison md intel edison grove gas mq2 https github com rwaldron johnny five blob main docs grove gas mq2 edison md intel edison grove humidity temperature th02 https github com rwaldron johnny five blob main docs grove humidity temperature edison md intel edison grove i2c motor driver https github com rwaldron johnny five blob main docs grove i2c motor driver edison md intel edison grove joystick https github com rwaldron johnny five blob main docs grove joystick edison md intel edison grove led https github com rwaldron johnny five blob main docs grove led edison md intel edison grove light sensor tsl2561 https github com rwaldron johnny five blob main docs grove light sensor edison md intel edison grove moisture sensor https github com rwaldron johnny five blob main docs grove moisture edison md intel edison grove q touch https github com rwaldron johnny five blob main docs grove q touch md intel edison grove rgb lcd https github com rwaldron johnny five blob main docs grove lcd rgb edison md intel edison grove rgb lcd color previewer https github com rwaldron johnny five blob main docs grove lcd rgb bgcolor previewer edison md intel edison grove rgb lcd temperature display https github com rwaldron johnny five blob main docs grove lcd rgb temperature display edison md intel edison grove relay https github com rwaldron johnny five blob main docs grove relay edison md intel edison grove rotary potentiometer https github com rwaldron johnny five blob main docs grove rotary potentiometer edison md intel edison grove servo https github com rwaldron johnny five blob main docs grove servo edison md intel edison grove touch https github com rwaldron johnny five blob main docs grove touch edison md grove iot kit seeed studio grove button https github com rwaldron johnny five blob main docs grove button md grove joystick https github com rwaldron johnny five blob main docs grove joystick md grove led https github com rwaldron johnny five blob main docs grove led md grove motor i2c driver https github com rwaldron johnny five blob main docs grove i2c motor driver md grove rgb lcd https github com rwaldron johnny five blob main docs grove lcd rgb md grove rgb lcd temperature display https github com rwaldron johnny five blob main docs grove lcd rgb temperature display md grove rotary potentiometer https github com rwaldron johnny five blob main docs grove rotary potentiometer md grove servo https github com rwaldron johnny five blob main docs grove servo md grove touch https github com rwaldron johnny five blob main docs grove touch md micro magician v2 micro magician v2 accelerometer https github com rwaldron johnny five blob main docs micromagician accelerometer md micro magician v2 motor https github com rwaldron johnny five blob main docs micromagician motor md micro magician v2 servo https github com rwaldron johnny five blob main docs micromagician servo md tinkerkit tinkerkit accelerometer https github com rwaldron johnny five blob main docs tinkerkit accelerometer md tinkerkit blink https github com rwaldron johnny five blob main docs tinkerkit blink md tinkerkit button https github com rwaldron johnny five blob main docs tinkerkit button md tinkerkit combo https github com rwaldron johnny five blob main docs tinkerkit combo md tinkerkit continuous servo https github com rwaldron johnny five blob main docs tinkerkit continuous servo md tinkerkit gyro https github com rwaldron johnny five blob main docs tinkerkit gyroscope md tinkerkit joystick https github com rwaldron johnny five blob main docs tinkerkit joystick md tinkerkit linear potentiometer https github com rwaldron johnny five blob main docs tinkerkit linear pot md tinkerkit rotary potentiometer https github com rwaldron johnny five blob main docs tinkerkit rotary md tinkerkit temperature https github com rwaldron johnny five blob main docs tinkerkit thermistor md tinkerkit tilt https github com rwaldron johnny five blob main docs tinkerkit tilt md tinkerkit touch https github com rwaldron johnny five blob main docs tinkerkit touch md wii wii classic controller https github com rwaldron johnny five blob main docs classic controller md wii nunchuck https github com rwaldron johnny five blob main docs nunchuk md complete bots projects bug https github com rwaldron johnny five blob main docs bug md kinect robotic arm controller https github com rwaldron johnny five blob main docs kinect arm controller md laser trip wire https github com rwaldron johnny five blob main docs laser trip wire md line follower https github com rwaldron johnny five blob main docs line follower md lynxmotion biped brat https github com rwaldron johnny five blob main docs brat md motobot https github com rwaldron johnny five blob main docs motobot md navigator https github com rwaldron johnny five blob main docs navigator md nodebot https github com rwaldron johnny five blob main docs nodebot md phoenix hexapod https github com rwaldron johnny five blob main docs phoenix md radar https github com rwaldron johnny five blob main docs radar md robotic claw https github com rwaldron johnny five blob main docs claw md whisker https github com rwaldron johnny five blob main docs whisker md component plugin template example plugin https github com rwaldron johnny five blob main docs plugin md io plugins led blink on electric imp https github com rwaldron johnny five blob main docs imp io md led blink on intel edison arduino board https github com rwaldron johnny five blob main docs edison io arduino md led blink on intel edison mini board https github com rwaldron johnny five blob main docs edison io miniboard md led blink on intel galileo gen 2 https github com rwaldron johnny five blob main docs galileo io md led blink on raspberry pi https github com rwaldron johnny five blob main docs raspi io md led blink on spark core https github com rwaldron johnny five blob main docs spark io md led blink on pcduino3 https github com rwaldron johnny five blob main docs pcduino io md extract end examples many fragments some large some small wireless nodebot http jsfiddle net rwaldron 88m6b show light kinect controlled robot arm http jsfiddle net rwaldron xmsgq show light biped nodebot http jsfiddle net rwaldron wzkn5 show light lcd running man http jsfiddle net rwaldron xkwau show light slider controlled panning servo http jsfiddle net rwaldron kzakv show light joystick controlled laser pan tilt 1 http jsfiddle net rwaldron hpqms show light joystick controlled laser pan tilt 2 http jsfiddle net rwaldron yhb7a show light joystick controlled claw http jsfiddle net rwaldron 6zxfe show light robot claw http jsfiddle net rwaldron cfszj show light joystick motor led http jsfiddle net rwaldron gadsz show light build you own drone http github com darioodiaz node open pilot make javascript robotics http ecx images amazon com images i 91ae8zzdq2l jpg http shop oreilly com product 0636920031390 do contributing all contributions must adhere to the idiomatic js style guide https github com rwaldron idiomatic js by maintaining the existing coding style add unit tests for any new or changed functionality lint and test your code using grunt https github com gruntjs grunt license copyright c 2012 2013 2014 rick waldron waldron rick gmail com licensed under the mit license copyright c 2014 2015 the johnny five contributors licensed under the mit license | javascript arduino tessel raspberry-pi chip photon pcduino gpio i2c adc dac pwm spi serial 1-wire bluetooth iot robotics beaglebone-black intel | 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cse4310 | cse 4310 introduction to computer vision learn to make this https github com cmcmurrough cse4310 blob master t800 jpg raw true overview as the title implies this course provides an introduction to computer vision honestly a better title would be introduction to computer vision programming because i emphasize practical coding examples over rigorous theory the outcome for this course is that all students should have a general understanding of how to combine common computer vision algorithms into more advanced processing pipelines of enhanced capability programming skills this class will involve regular fairly intensive programming assignments roughly 50 of the assignments will require c or python skills while the other 50 will require explicitly c all notes and example code will be written within the context of c in order to take this course you must either be confident in your current c abilities or your ability to teach yourself a new language quickly the programming assignments will require time focus and dedication to complete as your instructor i promise that you will learn valuable skills while solving the problems that i assign however i do expect you to spend a significant amount of work hours in a relatively short calendar period on programming grading my goal as the instructor of this class is to instill as many computer machine vision perception skills in you as possible in a relatively short mount of time i will grade you on the design of your solutions and the quality of your submitted code formatting adherence to standards commenting etc class attendance will not be recorded but absences will very likely negatively affect your performance on assignments earning a grade of a in the class will require functional quality code submissions on each assignment a thorough specificaion of requirements along with the corresponding grading weights is provided with each assignment some topics to be covered 1 introduction to state of the art computer vision techniques and applications 2 programming environment setup 3 data acquisition and display 4 image thresholding morphology 5 edges lines ransac 7 motion detection tracking 8 feature detection object recognition 9 optical character recognition 10 camera calibration fiducial markers 11 depth and 3d sensing techniques and hardware 19 point clouds 13 segmentation 14 registration 15 performance considerations 16 parallel processing 17 emerging technologies libraries and frameworks 1 opencv 2 point cloud library 3 cuda | ai |
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polish-nlp-resources | polish nlp resources this repository contains pre trained models and language resources for natural language processing in polish created during my research some of the models are also available on huggingface hub https huggingface co sdadas if you d like to use any of those resources in your research please cite bibtex misc polish nlp resources author s l awomir dadas title a repository of polish nlp resources howpublished github year 2019 url https github com sdadas polish nlp resources contents word embeddings word embeddings word2vec word2vec fasttext fasttext glove glove high dimensional word vectors high dimensional word vectors compressed word2vec compressed word2vec wikipedia2vec wikipedia2vec language models language models elmo elmo roberta roberta bart bart gpt 2 gpt 2 longformer longformer text encoders text encoders machine translation models machine translation models convolutional models for fairseq convolutional models for fairseq t5 based models t5 based models fine tuned models fine tuned models dictionaries and lexicons dictionaries and lexicons links to external resources links to external resources repositories of linguistic tools and resources repositories of linguistic tools and resources publicly available large polish text corpora 1gb publicly available large polish text corpora 1gb models supporting polish language models supporting polish language word embeddings the following section includes pre trained word embeddings for polish each model was trained on a corpus consisting of polish wikipedia dump polish books and articles 1 5 billion tokens at total word2vec word2vec trained with gensim https radimrehurek com gensim 100 dimensions negative sampling contains lemmatized words with 3 or more ocurrences in the corpus and additionally a set of pre defined punctuation symbols all numbers from 0 to 10 000 polish forenames and lastnames the archive contains embedding in gensim binary format example of usage python from gensim models import keyedvectors if name main word2vec keyedvectors load word2vec polish bin print word2vec similar by word bierut cyrankiewicz 0 818274736404419 gomu ka 0 7967918515205383 raczkiewicz 0 7757788896560669 jaruzelski 0 7737460732460022 pu ak 0 7667238712310791 download github https github com sdadas polish nlp resources releases download v1 0 word2vec zip fasttext fasttext trained with gensim https radimrehurek com gensim vocabulary and dimensionality is identical to word2vec model the archive contains embedding in gensim binary format example of usage python from gensim models import keyedvectors if name main word2vec keyedvectors load fasttext 100 3 polish bin print word2vec similar by word bierut bieruty 0 9290274381637573 gierut 0 8921363353729248 bieruta 0 8906412124633789 bierutow 0 8795544505119324 bierutowsko 0 839280366897583 download onedrive https witedupl my sharepoint com u g personal dadass wit edu pl eeodv cq0ktaupma0e9iilebmtvvw4ozabbpuaxumfd8ea e 5naf5z glove global vectors for word representation glove trained using the reference implementation from stanford nlp 100 dimensions contains lemmatized words with 3 or more ocurrences in the corpus example of usage python from gensim models import keyedvectors if name main word2vec keyedvectors load word2vec format glove 100 3 polish txt print word2vec similar by word bierut cyrankiewicz 0 8335597515106201 gomu ka 0 7793121337890625 bieruta 0 7118682861328125 jaruzelski 0 6743760108947754 minc 0 6692837476730347 download github https github com sdadas polish nlp resources releases download v1 0 glove zip high dimensional word vectors pre trained vectors using the same vocabulary as above but with higher dimensionality these vectors are more suitable for representing larger chunks of text such as sentences or documents using simple word aggregation methods averaging max pooling etc as more semantic information is preserved this way glove 300d part 1 github https github com sdadas polish nlp resources releases download glove hd glove 300 3 polish zip 001 500d part 1 github https github com sdadas polish nlp resources releases download glove hd glove 500 3 polish zip 001 part 2 github https github com sdadas polish nlp resources releases download glove hd glove 500 3 polish zip 002 800d part 1 github https github com sdadas polish nlp resources releases download glove hd glove 800 3 polish zip 001 part 2 github https github com sdadas polish nlp resources releases download glove hd glove 800 3 polish zip 002 part 3 github https github com sdadas polish nlp resources releases download glove hd glove 800 3 polish zip 003 word2vec 300d onedrive https witedupl my sharepoint com u g personal dadass wit edu pl eq7qa6pkpupbtzyyp8kaafmb0z9fdhfme7kxm tcrwh9ha e rgekmu 500d onedrive https witedupl my sharepoint com u g personal dadass wit edu pl efbt7wvy7evhuqziuupnjzsbxtn2l896ldvvhrbicmuh a e f0lgvc 800d onedrive https witedupl my sharepoint com u g personal dadass wit edu pl eda0vukicnpnk4omf2eolzkbtjbmtmymkqqz yoexw98ta e rku4pp fasttext 300d onedrive https witedupl my sharepoint com u g personal dadass wit edu pl esj0xtxmtk5jhiocp5oxt7ibuumaejczfwvqn17c2qngcg e 9aory9 500d onedrive https witedupl my sharepoint com u g personal dadass wit edu pl evivrrf38fjmv1ihx2ardnebffoe mlsdhccmg49iqeccq e g36nj7 800d onedrive https witedupl my sharepoint com u g personal dadass wit edu pl eshkej7jlglhoaikiydl0nkb z8vjyfcehx3tpe7l1knfg e fkobga compressed word2vec this is a compressed version of the word2vec embedding model described above for compression we used the method described in compressing word embeddings via deep compositional code learning https arxiv org abs 1711 01068 by shu and nakayama compressed embeddings are suited for deployment on storage poor devices such as mobile phones the model weights 38mb only 4 4 size of the original word2vec embeddings although the authors of the article claimed that compressing with their method doesn t hurt model performance we noticed a slight but acceptable drop of accuracy when using compressed version of embeddings sample decoder class with usage python import gzip from typing import dict callable import numpy as np class compressedembedding object def init self vocab path str embedding path str to lowercase bool true self vocab path str vocab path self embedding path str embedding path self to lower bool to lowercase self vocab dict str int self load vocab vocab path embedding np load embedding path self codes np ndarray embedding embedding files 0 self codebook np ndarray embedding embedding files 1 self m self codes shape 1 self k int self codebook shape 0 self m self dim int self codebook shape 1 def load vocab self vocab path str dict str int open func callable gzip open if vocab path endswith gz else open with open func vocab path rt encoding utf 8 as input file return line strip idx for idx line in enumerate input file def vocab vector self word str if word pad return np zeros self dim val str word lower if self to lower else word index int self vocab get val self vocab unk codes self codes index code indices np array idx self k offset for idx offset in enumerate np nditer codes return np sum self codebook code indices axis 0 if name main word2vec compressedembedding word2vec 100 3 vocab gz word2vec 100 3 compressed npz print word2vec vocab vector bierut download github https github com sdadas polish nlp resources releases download v1 0 compressed zip wikipedia2vec wikipedia2vec https wikipedia2vec github io is a toolkit for learning joint representations of words and wikipedia entities we share polish embeddings learned using a modified version of the library in which we added lemmatization and fixed some issues regarding parsing wiki dumps for languages other than english embedding models are available in sizes from 100 to 800 dimensions a simple example python from wikipedia2vec import wikipedia2vec wiki2vec wikipedia2vec load wiki2vec plwiki 100 bin print wiki2vec most similar wiki2vec get entity boles aw bierut entity boles aw bierut 1 0 word bierut 0 75790733 word gomu ka 0 7276504 entity krajowa rada narodowa 0 7081445 entity w adys aw gomu ka 0 7043667 download embeddings 100d https witedupl my sharepoint com u g personal dadass wit edu pl ee dfnilujxcihmfujrsqzubbpst44eyctmanpb tq ykw e zzwiuf 300d https witedupl my sharepoint com u g personal dadass wit edu pl ewbzb1a89yjjku3vzfpobtub5wtnaqisznkt2aaksp6xdq e hhxsf0 500d https witedupl my sharepoint com u g personal dadass wit edu pl erysjueo dlkpubbv a86 0brhdb88tjgr wtzbkxhfjg e bpjh80 800d https witedupl my sharepoint com u g personal dadass wit edu pl eqjt8qyrmlfeqtc 1zdoi54bzoqxilvoqibhra9euiov7w e slfqri language models elmo embeddings from language models elmo is a contextual embedding presented in deep contextualized word representations https arxiv org abs 1802 05365 by peters et al sample usage with pytorch below for a more detailed instructions for integrating elmo with your model please refer to the official repositories github com allenai bilm tf https github com allenai bilm tf tensorflow and github com allenai allennlp https github com allenai allennlp pytorch python from allennlp commands elmo import elmoembedder elmo elmoembedder options json weights hdf5 print elmo embed sentence za ci g l ja download github https github com sdadas polish nlp resources releases download v1 0 elmo zip roberta language model for polish based on popular transformer architecture we provide weights for improved bert language model introduced in roberta a robustly optimized bert pretraining approach https arxiv org pdf 1907 11692 pdf we provide two roberta models for polish base and large model a summary of pre training parameters for each model is shown in the table below we release two version of the each model one in the fairseq https github com pytorch fairseq format and the other in the huggingface transformers https github com huggingface transformers format more information about the models can be found in a separate repository https github com sdadas polish roberta table thead th model th th l h a th th batch size th th update steps th th corpus size th th fairseq th th transformers th thead tr td roberta nbsp base td td 12 nbsp nbsp 768 nbsp nbsp 12 td td 8k td td 125k td td 20gb td td a href https github com sdadas polish roberta releases download models roberta base fairseq zip v0 9 0 a td td a href https github com sdadas polish roberta releases download models transformers v3 4 0 roberta base transformers zip v3 4 a td tr tr td roberta 8209 v2 nbsp base td td 12 nbsp nbsp 768 nbsp nbsp 12 td td 8k td td 400k td td 20gb td td a href https github com sdadas polish roberta releases download models v2 roberta base fairseq zip v0 10 1 a td td a href https github com sdadas polish roberta releases download models v2 roberta base transformers zip v4 4 a td tr tr td roberta nbsp large td td 24 nbsp nbsp 1024 nbsp nbsp 16 td td 30k td td 50k td td 135gb td td a href https github com sdadas polish roberta releases download models roberta large fairseq zip v0 9 0 a td td a href https github com sdadas polish roberta releases download models transformers v3 4 0 roberta large transformers zip v3 4 a td tr tr td roberta 8209 v2 nbsp large td td 24 nbsp nbsp 1024 nbsp nbsp 16 td td 2k td td 400k td td 200gb td td a href https github com sdadas polish roberta releases download models v2 roberta large fairseq zip v0 10 2 a td td a href https github com sdadas polish roberta releases download models v2 roberta large transformers zip v4 14 a td tr tr tr td distilroberta td td 6 nbsp nbsp 768 nbsp nbsp 12 td td 1k td td 10ep td td 20gb td td n a td td a href https github com sdadas polish roberta releases download models v2 distilroberta transformers zip v4 13 a td tr table l the number of encoder blocks h hidden size a the number of attention heads br example in fairseq python import os from fairseq models roberta import robertamodel robertahubinterface from fairseq import hub utils model path roberta large fairseq loaded hub utils from pretrained model name or path model path data name or path model path bpe sentencepiece sentencepiece vocab os path join model path sentencepiece bpe model load checkpoint heads true archive map robertamodel hub models cpu true roberta robertahubinterface loaded args loaded task loaded models 0 roberta eval roberta fill mask druga wojna wiatowa zako czy a si w mask roku topk 1 roberta fill mask ludzie najbardziej boj si mask topk 1 druga wojna wiatowa zako czy a si w 1945 roku 0 9345270991325378 1945 ludzie najbardziej boj si mierci 0 14140743017196655 mierci it is recommended to use the above models but it is still possible to download our old model https github com sdadas polish nlp resources releases download roberta roberta zip trained on smaller batch size 2k and smaller corpus 15gb bart bart is a transformer based sequence to sequence model trained with a denoising objective can be used for fine tuning on prediction tasks just like regular bert as well as various text generation tasks such as machine translation summarization paraphrasing etc we provide a polish version of bart base model trained on a large corpus of texts extracted from common crawl 200 gb more information on the bart architecture can be found in bart denoising sequence to sequence pre training for natural language generation translation and comprehension https arxiv org abs 1910 13461 example in hugginface transformers python import os from transformers import bartforconditionalgeneration pretrainedtokenizerfast model dir bart base transformers tok pretrainedtokenizerfast tokenizer file os path join model dir tokenizer json model bartforconditionalgeneration from pretrained model dir sent druga mask wiatowa zako czy a si w mask roku kapitulacj hitlerowskich mask batch tok sent return tensors pt generated ids model generate batch input ids print tok batch decode generated ids skip special tokens true druga wojna wiatowa zako czy a si w 1945 roku kapitulacj hitlerowskich niemiec download for fairseq v0 10 https github com sdadas polish nlp resources releases download bart base bart base fairseq zip or huggingface transformers v4 0 https github com sdadas polish nlp resources releases download bart base bart base transformers zip gpt 2 gpt 2 is a unidirectional transformer based language model trained with an auto regressive objective originally introduced in the language models are unsupervised multitask learners https d4mucfpksywv cloudfront net better language models language models are unsupervised multitask learners pdf paper the original english gpt 2 was released in four sizes differing by the number of parameters small 112m medium 345m large 774m xl 1 5b models for huggingface transformers we provide polish gpt 2 models for huggingface transformers the models have been trained using megatron lm https github com nvidia megatron lm library and then converted to the huggingface format the released checkpoints support longer contexts than the original gpt 2 by openai small and medium models support up to 2048 tokens twice as many as gpt 2 models and the same as gpt 3 large and xl models support up to 1536 tokens example in transformers python from transformers import pipeline generator pipeline text generation model sdadas polish gpt2 medium results generator policja skontrolowa a trze wo kierowc w max new tokens 1024 do sample true repetition penalty 1 2 num return sequences 1 num beams 1 temperature 0 95 top k 50 top p 0 95 print results 0 get generated text policja skontrolowa a trze wo kierowc w teraz policjanci przypominaj kierowcom o zachowaniu bezpiecznej odleg o ci i rodkach ostro no ci zwi zanych z pandemi kieruj cy po spo yciu alkoholu s bardziej wyczuleni na innych uczestnik w ruchu drogowego oraz maj wi ksz sk onno do brawury i ryzykownego zachowania zw aszcza wobec pieszych dodatkowo nie zawsze pami taj oni zasady obowi zuj cych u nas przepis w prawa reguluj cych kwestie dotycz ce odpowiedzialno ci small https huggingface co sdadas polish gpt2 small medium https huggingface co sdadas polish gpt2 medium large https huggingface co sdadas polish gpt2 large and xl https huggingface co sdadas polish gpt2 xl models are available on the huggingface hub models for fairseq we provide polish versions of the medium and large gpt 2 models trained using fairseq library example in fairseq python import os from fairseq import hub utils from fairseq models transformer lm import transformerlanguagemodel model dir gpt2 medium fairseq loaded hub utils from pretrained model name or path model dir checkpoint file model pt data name or path model dir bpe hf byte bpe bpe merges os path join model dir merges txt bpe vocab os path join model dir vocab json load checkpoint heads true archive map transformerlanguagemodel hub models model hub utils generatorhubinterface loaded args loaded task loaded models model eval result model sample policja skontrolowa a trze wo kierowc w beam 5 sampling true sampling topk 50 sampling topp 0 95 temperature 0 95 max len a 1 max len b 100 no repeat ngram size 3 print result 0 policja skontrolowa a trze wo kierowc w pojazd w wszystko dzia o si na drodze gminnej mi dzy radwanowem a boguchowem oko o godziny 12 30 do naszego komisariatu zg osi si kierowca kt rego zaniepokoi o zachowanie kieruj cego w chwili wjazdu na t drog prawdopodobnie nie mia zapi tych pas w informuje st asp anna w grzyniak z policji w brzezinach okaza o si e kieruj cy by pod wp ywem alkoholu download medium https github com sdadas polish nlp resources releases download gpt 2 gpt2 medium fairseq 7z or large https github com sdadas polish nlp resources releases download gpt 2 gpt2 large fairseq 7z model for fairseq v0 10 longformer one of the main constraints of standard transformer architectures is the limitation on the number of input tokens there are several known models that allow processing of long documents one of the popular ones being longformer introduced in the paper longformer the long document transformer https arxiv org abs 2004 05150 we provide base and large versions of polish longformer model the models were initialized with polish roberta v2 weights and then fine tuned on a corpus of long documents ranging from 1024 to 4096 tokens example in huggingface transformers python from transformers import pipeline fill mask pipeline fill mask model sdadas polish longformer base 4096 fill mask stolica oraz najwi ksze miasto francji to mask base https huggingface co sdadas polish longformer base 4096 and large https huggingface co sdadas polish longformer large 4096 models are available on the huggingface hub text encoders the purpose of text encoders is to produce a fixed length vector representation for chunks of text such as sentences or paragraphs these models are used in semantic search question answering document clustering dataset augmentation plagiarism detection and other tasks which involve measuring semantic similarity or relatedness between text passages paraphrase mining and semantic textual similarity we share two models based on the sentence transformers https www sbert net library trained using distillation method described in the paper making monolingual sentence embeddings multilingual using knowledge distillation https arxiv org abs 2004 09813 a corpus of 100 million parallel polish english sentence pairs from the opus https opus nlpl eu project was used to train the models you can download them from the hugginface hub using the links below table thead th student model th th teacher model th th download th thead tr td polish roberta base v2 td td paraphrase distilroberta base v2 td td a href https huggingface co sdadas st polish paraphrase from distilroberta st polish paraphrase from distilroberta a td tr tr td polish roberta base v2 td td paraphrase mpnet base v2 td td a href https huggingface co sdadas st polish paraphrase from mpnet st polish paraphrase from mpnet a td tr table a simple example in sentence transformers library python from sentence transformers import sentencetransformer from sentence transformers util import cos sim sentences bardzo lubi je s odycze uwielbiam zajada si s odko ciami model sentencetransformer sdadas st polish paraphrase from mpnet results model encode sentences convert to tensor true show progress bar false print cos sim results 0 results 1 tensor 0 9794 device cuda 0 information retrieval mmlw musz mie lepsz wiadomo is a set of text encoders trained using multilingual knowledge distillation method https arxiv org abs 2004 09813 on a diverse corpus of 60 million polish english text pairs which included both sentence and paragraph aligned translations the encoders are available in sentence transformers https www sbert net format we provide five encoders optimized for text information retrieval tasks the models were trained using a two step process in the first step the encoders were initialized with polish roberta and multilingual e5 checkpoints and then distilled utilising english bge as a teacher model the second step involved fine tuning the obtained models on polish ms marco https huggingface co datasets clarin knext msmarco pl dataset with contrastrive loss in the table below we present the details of the released models table thead th student model th th teacher model th th pirb br ndcg 10 th th download th thead tr td colspan 4 strong encoders based on polish roberta strong td tr tr td a href https huggingface co sdadas polish roberta base v2 polish roberta base v2 a td td a href https huggingface co baai bge base en bge base en a td td 56 24 td td a href https huggingface co sdadas mmlw retrieval roberta base mmlw retrieval roberta base a td tr tr td a href https huggingface co sdadas polish roberta large v2 polish roberta large v2 a td td a href https huggingface co baai bge large en bge large en a td td 58 15 td td a href https huggingface co sdadas mmlw retrieval roberta large mmlw retrieval roberta large a td tr tr td colspan 4 strong encoders based on multilingual e5 strong td tr tr td a href https huggingface co intfloat multilingual e5 small multilingual e5 small a td td a href https huggingface co baai bge small en bge small en a td td 52 34 td td a href https huggingface co sdadas mmlw retrieval e5 small mmlw retrieval e5 small a td tr tr td a href https huggingface co intfloat multilingual e5 base multilingual e5 base a td td a href https huggingface co baai bge base en bge base en a td td 56 09 td td a href https huggingface co sdadas mmlw retrieval e5 base mmlw retrieval e5 base a td tr tr td a href https huggingface co intfloat multilingual e5 large multilingual e5 large a td td a href https huggingface co baai bge large en bge large en a td td 58 05 td td a href https huggingface co sdadas mmlw retrieval e5 large mmlw retrieval e5 large a td tr table please note that the developed models require the use of specific prefixes and suffixes when encoding texts for roberta based encoders each query should be preceded by the prefix zapytanie and no prefix is needed for passages for e5 based models queries should be prefixed with query and passages with passage an example of how to use the models python from sentence transformers import sentencetransformer from sentence transformers util import cos sim query prefix zapytanie zapytanie for roberta query for e5 answer prefix empty for roberta passage for e5 queries query prefix jak do y 100 lat answers answer prefix trzeba zdrowo si od ywia i uprawia sport answer prefix trzeba pi alkohol imprezowa i je dzi szybkimi autami answer prefix gdy trwa a kampania politycy zapewniali e rozprawi si z zakazem niedzielnego handlu model sentencetransformer sdadas mmlw retrieval roberta base queries emb model encode queries convert to tensor true show progress bar false answers emb model encode answers convert to tensor true show progress bar false best answer cos sim queries emb answers emb argmax item print answers best answer trzeba zdrowo si od ywia i uprawia sport machine translation models this section includes pre trained machine translation models convolutional models for fairseq we provide polish english and english polish convolutional neural machine translation models trained using fairseq https github com pytorch fairseq sequence modeling toolkit both models were trained on a parallel corpus of more than 40 million sentence pairs taken from opus http opus nlpl eu collection example of usage fairseq sacremoses and subword nmt python packages are required to run this example python from fairseq models import basefairseqmodel model path polish english model basefairseqmodel from pretrained model name or path model path checkpoint file checkpoint best pt data name or path model path tokenizer moses bpe subword nmt bpe codes code cpu true print model translate sentence zesp astronom w odkry w konstelacji panny niezwyk planet beam 5 a team of astronomers discovered an extraordinary planet in the constellation of virgo polish english convolutional model download github https github com sdadas polish nlp resources releases download nmt models conv polish english conv zip english polish convolutional model download github https github com sdadas polish nlp resources releases download nmt models conv english polish conv zip t5 based models we share mt5 and flan t5 models fine tuned for polish english and english polish translation the models were trained on 70 million sentence pairs from opus http opus nlpl eu you can download them from the hugginface hub using the links below an example of how to use the models python from transformers import pipeline generator pipeline translation model sdadas flan t5 base translator en pl sentence a team of astronomers discovered an extraordinary planet in the constellation of virgo print generator sentence max length 512 translation text zesp astronom w odkry niezwyk planet w gwiazdozbiorze panny the following models are available on the huggingface hub mt5 base translator en pl https huggingface co sdadas mt5 base translator en pl mt5 base translator pl en https huggingface co sdadas mt5 base translator pl en flan t5 base translator en pl https huggingface co sdadas flan t5 base translator en pl fine tuned models byt5 text correction a small multilingual utility model intended for simple text correction it is designed to improve the quality of texts from the web often lacking punctuation or proper word capitalization the model was trained to perform three types of corrections restoring punctuation in sentences restoring word capitalization and restoring diacritical marks for languages that include them the following languages are supported belarusian be danish da german de greek el english en spanish es french fr italian it dutch nl polish pl portuguese pt romanian ro russian ru slovak sk swedish sv ukrainian uk the model takes as input a sentence preceded by a language code prefix for example python from transformers import pipeline generator pipeline text2text generation model sdadas byt5 text correction sentences pl ciekaw jestem na co licza onuce stawiajace na sykulskiego w nadziei na zwrot ku rosji de die frage die sich die europ er stellen m ssen lautet ist es in unserem interesse die krise auf taiwan zu beschleunigen ru 26 1910 generator sentences max length 512 ciekaw jestem na co licz onuce stawiaj ce na sykulskiego w nadziei na zwrot ku rosji die frage die sich die europ er stellen m ssen lautet ist es in unserem interesse die krise auf taiwan zu beschleunigen 26 1910 the model is available on the huggingface hub byt5 text correction https huggingface co sdadas byt5 text correction dictionaries and lexicons polish english and foreign person names this lexicon contains 346 thousand forenames and lastnames labeled as polish english or foreign other crawled from multiple internet sources possible labels are p n polish forename p l polish lastname e n english forename e l english lastname f foreign other for each word there is an additional flag indicating whether this name is also used as a common word in polish c for common u for uncommon download github lexicons names named entities extracted from sjp pl this dictionary consists mostly of the names of settlements geographical regions countries continents and words derived from them relational adjectives and inhabitant names besides that it also contains names of popular brands companies and common abbreviations of institutions names this resource was created in a semi automatic way by extracting the words and their forms from sjp pl using a set of heuristic rules and then manually filtering out words that weren t named entities download github lexicons named sjp links to external resources repositories of linguistic tools and resources computational linguistics in poland ipi pan http clip ipipan waw pl lrt g4 19 research group wroclaw university of technology http nlp pwr wroc pl narzedzia i zasoby clarin repository of linguistic resources https clarin pl eu dspace gonito net evaluation platform with some challenges for polish https gonito net awesome nlp polish ksopyla https github com ksopyla awesome nlp polish publicly available large polish text corpora 1gb oscar corpus common crawl extract https oscar corpus com cc 100 web crawl data common crawl extract http data statmt org cc 100 the polish parliamentary corpus http clip ipipan waw pl ppc redistributable subcorpora of the national corpus of polish http zil ipipan waw pl distrnkjp polish wikipedia dumps https dumps wikimedia org plwiki opus parallel corpora https opus nlpl eu corpus from poleval 2018 language modeling task http 2018 poleval pl index php tasks c4 and mc4 corpora contains 180gb of compressed polish text https huggingface co datasets allenai c4 nllb parallel corpus 1613 language pairs of which 43 include polish https huggingface co datasets allenai nllb culturax a combination of mc4 and oscar corpora cleaned and deduplicated https huggingface co datasets uonlp culturax models supporting polish language sentence analysis tokenization lemmatization pos tagging etc spacy https spacy io models pl a popular library for nlp in python which includes polish models for sentence analysis stanza https stanfordnlp github io stanza a collection of neural nlp models for many languages from stndordnlp trankit https github com nlp uoregon trankit a light weight transformer based python toolkit for multilingual natural language processing by the university of oregon krnnt https github com kwrobel nlp krnnt and kftt https github com kwrobel nlp kftt neural morphosyntactic taggers for polish morfeusz http morfeusz sgjp pl a classic polish morphosyntactic tagger language tool https github com languagetool org languagetool java based open source proofreading software for many languages with sentence analysis tools included stempel https github com dzieciou pystempel algorythmic stemmer for polish polemma https huggingface co amu cai polemma large plt5 based lemmatizer of named entities and multi word expressions for polish available in small https huggingface co amu cai polemma small base https huggingface co amu cai polemma base and large https huggingface co amu cai polemma large sizes machine translation marian nmt https marian nmt github io an efficient c based implementation of neural translation models many pre trained models are available including those supporting polish pl de https huggingface co helsinki nlp opus mt pl de pl en https huggingface co helsinki nlp opus mt pl en pl es https huggingface co helsinki nlp opus mt pl es pl fr https huggingface co helsinki nlp opus mt pl fr pl sv https huggingface co helsinki nlp opus mt pl sv de pl https huggingface co helsinki nlp opus mt de pl es pl https huggingface co helsinki nlp opus mt es pl fr pl https huggingface co helsinki nlp opus mt fr pl m2m https github com pytorch fairseq tree master examples m2m 100 2021 a single massive machine translation architecture supporting direct translation for any pair from the list of 100 languages details in the paper beyond english centric multilingual machine translation https arxiv org pdf 2010 11125 pdf mbart 50 https huggingface co facebook mbart large 50 many to many mmt 2021 a multilingual bart model fine tuned for machine translation in 50 languages three machine translation models were published many to many https huggingface co facebook mbart large 50 many to many mmt english to many https huggingface co facebook mbart large 50 one to many mmt and many to english https huggingface co facebook mbart large 50 many to one mmt for more information see multilingual translation with extensible multilingual pretraining and finetuning https arxiv org abs 2008 00401 nllb https github com facebookresearch fairseq tree nllb 2022 nllb no language left behind is a project by meta ai aiming to provide machine translation models for over 200 languages a set of multilingual neural models ranging from 600m to 54 5b parameters is available for download for more details see no language left behind scaling human centered machine translation https research facebook com publications no language left behind language models multilingual bert https github com google research bert blob master multilingual md 2018 bert bidirectional encoder representations from transformers is a model for generating contextual word representations multilingual cased model provided by google supports 104 languages including polish xlm roberta https github com pytorch fairseq tree master examples xlmr 2019 cross lingual language model trained on 2 5 terabytes of data from commoncrawl and wikipedia supports 100 languages including polish see unsupervised cross lingual representation learning at scale https arxiv org pdf 1911 02116 pdf for details slavic bert https github com deepmipt slavic bert ner slavic bert 2019 multilingual bert model supporting bulgarian bg czech cs polish pl and russian ru languages mt5 https github com google research multilingual t5 2020 google s text to text transformer for 101 languages based on the t5 architecture details in the paper mt5 a massively multilingual pre trained text to text transformer https arxiv org abs 2010 11934 herbert https huggingface co allegro 2020 polish bert based language model trained by allegro for huggingface transformers in base https huggingface co allegro herbert base cased and large https huggingface co allegro herbert large cased variant plt5 https huggingface co allegro plt5 large 2021 polish version of the t5 model available in small https huggingface co allegro plt5 small base https huggingface co allegro plt5 base and large https huggingface co allegro plt5 large sizes byt5 https huggingface co docs transformers model doc byt5 2021 a multilignual sequence to sequence model similar to t5 but using raw byte sequences as inputs instead of subword tokens introduced in the paper byt5 towards a token free future with pre trained byte to byte models https arxiv org abs 2105 13626 xlm roberta xl and xxl https github com pytorch fairseq blob main examples xlmr readme md 2021 large scale versions of xlm roberta models with 3 5 and 10 7 billion parameters respectively for more information see larger scale transformers for multilingual masked language modeling https arxiv org pdf 2105 00572 pdf mluke https huggingface co docs transformers model doc mluke 2021 a multilingual version of luke transformer based language model enriched with entity metadata the model supports 24 languages including polish for more information see mluke the power of entity representations in multilingual pretrained language models https arxiv org pdf 2110 08151 pdf xglm https huggingface co facebook xglm 4 5b 2021 a gpt style autoregressive transformer language model trained on a large scale multilingual corpus the model was published in several sizes but only the 4 5b variant includes polish language for more information see few shot learning with multilingual language models https arxiv org abs 2112 10668 papugapt2 https huggingface co flax community papugapt2 2021 polish gpt like autoregressive models available in base https huggingface co flax community papugapt2 and large https huggingface co flax community papugapt2 large sizes mgpt https huggingface co sberbank ai mgpt 2022 another multilingual gpt style model with 1 3b parameters covering 60 languages the model has been trained by sberbank ai for more information see mgpt few shot learners go multilingual https arxiv org abs 2204 07580 flan t5 https huggingface co docs transformers model doc flan t5 2022 an improved version of t5 model fine tuned on a broad set of downstream tasks in multiple languages flan t5 models can be used in zero shot and few shot scenarios they can also be further fine tuned for specific task for more information see scaling instruction finetuned language models https arxiv org pdf 2210 11416 pdf xlm v https huggingface co facebook xlm v base 2023 a multilingual transformer based language model utilising large vocabulary of 1 million tokens which brings significant improvements on downstream tasks for some languages apart from a larger vocabulary the model s architecture is similar to previously published xlm r models for more information see xlm v overcoming the vocabulary bottleneck in multilingual masked language models https arxiv org abs 2301 10472 umt5 https console cloud google com storage browser t5 data pretrained models t5x umt5 xxl 2023 an improved mt5 model trained using a more uniform language distribution for more information see unimax fairer and more effective language sampling for large scale multilingual pretraining https arxiv org pdf 2304 09151 pdf mlongt5 https console cloud google com storage browser t5 data pretrained models t5x mlongt5 2023 a multilingual version of longt5 which is an extension of the t5 model that handles long inputs of up to 16k tokens supports 101 languages including polish for more information see mlongt5 a multilingual and efficient text to text transformer for longer sequences https arxiv org pdf 2305 11129 pdf sentence encoders universal sentence encoder https tfhub dev google universal sentence encoder multilingual large 1 2019 use universal sentence encoder generates sentence level langauge representations pre trained multilingual model supports 16 langauges arabic chinese simplified chinese traditional english french german italian japanese korean dutch polish portuguese spanish thai turkish russian laser language agnostic sentence representations https github com facebookresearch laser 2019 a multilingual sentence encoder by facebook research supporting 93 languages labse https tfhub dev google labse 1 2020 language agnostic bert sentence embedding model supporting 109 languages see language agnostic bert sentence embedding https arxiv org abs 2007 01852 for details sentence transformers https github com ukplab sentence transformers 2020 sentence level models based on the transformer architecture the library includes multilingual models supporting polish more information on multilingual knowledge distillation method used by the authors can be found in making monolingual sentence embeddings multilingual using knowledge distillation https arxiv org abs 2004 09813 laser2 and laser3 https github com facebookresearch laser blob main nllb readme md 2022 new versions of the laser sentence encoder by meta ai developed as a part of the nllb no language left behind project laser2 supports the same set of languages as the first version of the encoder which includes polish laser3 adds support to less common languages mostly low resource african languages see bitext mining using distilled sentence representations for low resource languages https arxiv org pdf 2205 12654 pdf for more details e5 https huggingface co intfloat multilingual e5 large 2022 a general purpose text encoder which can be applied to a variety of tasks such as information retrieval semantic textual similarity text reranking or clustering the models were trained using a large dataset of text pairs extracted from commoncrawl recently multilingual e5 models supporting polish have been published in small https huggingface co intfloat multilingual e5 small base https huggingface co intfloat multilingual e5 base and large https huggingface co intfloat multilingual e5 large versions see text embeddings by weakly supervised contrastive pre training https arxiv org abs 2212 03533 for more details optical character recognition ocr easy ocr https github com jaidedai easyocr optical character recognition toolkit with pre trained models for over 40 languages including polish tesseract https github com tesseract ocr tesseract popular ocr software developed since 1980s supporting over 100 languages for integration with python wrappers such as pytesseract https github com madmaze pytesseract or ocrmypdf https github com ocrmypdf ocrmypdf can be used speech processing speech recognition text to speech voice cloning etc quartznet nvidia nemo https catalog ngc nvidia com orgs nvidia teams nemo models stt pl quartznet15x5 2021 nvidia nemo is a toolkit for building conversational ai models apart from the framework itself nvidia also published many models trained using their code which includes a speech recognition model for polish based on quartznet architecture xls r https huggingface co facebook wav2vec2 xls r 300m 2021 xls r is a multilingual version of wav2vec 2 0 model by meta ai which is a large scale pre trained model for speech processing the model is trained in a self supervised way so it needs to be fine tuned for solving specific tasks such as asr several fine tuned checkpoints for polish speech recognition exist on the huggingface hub e g wav2vec2 large xlsr 53 polish https huggingface co jonatasgrosman wav2vec2 large xlsr 53 polish m ctc t https huggingface co speechbrain m ctc t large 2021 a speech recognition model from meta ai supporting 60 languages including polish for more information see pseudo labeling for massively multilingual speech recognition https arxiv org abs 2111 00161 whisper https github com openai whisper 2022 whisper is a model released by openai for asr and other speech related tasks supporting 82 languages the model is available in five sizes tiny 39m params base 74m small 244m medium 769m and large 1 5b more information can be found in the paper robust speech recognition via large scale weak supervision https cdn openai com papers whisper pdf mms https github com facebookresearch fairseq blob main examples mms readme md 2023 massively multilingual speech mms is a large scale multilingual speech foundation model published by meta ai along with the pre trained models they also released checkpoints fine tuned for specific tasks such as speech recognition https github com facebookresearch fairseq blob main examples mms readme md asr text to speech https github com facebookresearch fairseq blob main examples mms readme md tts and language identification https github com facebookresearch fairseq blob main examples mms readme md lid for more information see scaling speech technology to 1 000 languages https research facebook com publications scaling speech technology to 1000 languages seamlessm4t https github com facebookresearch seamless communication 2023 multilingual and multitask model trained on text and speech data it covers almost 100 languages including polish and can perform automatic speech recognition asr as well as multimodal translation tasks across languages speech to text text to speech speech to speech text to text see seamlessm4t massively multilingual multimodal machine translation https dl fbaipublicfiles com seamless seamless m4t paper pdf for more details sonar https github com facebookresearch sonar supported languages and download links 2023 multilingual embeddings for speech and text with a set of additional models fine tuned for specific tasks such as text translation speech to text translation or cross lingual semantic similarity see sonar sentence level multimodal and language agnostic representations https ai meta com research publications sonar sentence level multimodal and language agnostic representations for more details xtts https huggingface co coqui xtts v1 2023 a text to speech model that allows voice cloning by using just a 3 second audio sample of the target voice supports 13 languages including polish multimodal models multilingual clip sbert https huggingface co sentence transformers clip vit b 32 multilingual v1 2021 clip contrastive language image pre training is a neural network introducted by openai https github com openai clip which enables joint vector representations for images and text it can be used for building image search engines this is a multilingual version of clip trained by the authors of the sentence transformers https www sbert net library multilingual clip m clip https huggingface co m clip m bert base vit b 2021 this is yet another multilingual version of clip supporting polish language trained by the swedish institute of computer science sics layoutxlm https huggingface co microsoft layoutxlm base 2021 a multilingual version of layoutlmv2 https huggingface co docs transformers model doc layoutlmv2 model pre trained on 30 million documents in 53 languages the model combines visual spatial and textual modalities to solve prediction problems on visually rich documents such as pdfs or docs see layoutlmv2 multi modal pre training for visually rich document understanding https arxiv org abs 2012 14740 and layoutxlm multimodal pre training for multilingual visually rich document understanding https arxiv org abs 2104 08836 for details | natural-language-processing polish-language polish word-embedding lexicons machine-learning language-models | ai |
web | this repository has been migrated into overleaf overleaf https github com overleaf overleaf see the monorepo announcement https github com overleaf overleaf issues 923 for more info overleaf web overleaf web is the front end web service of the open source web based collaborative latex editor overleaf https www overleaf com it serves all the html pages css and javascript to the client overleaf web also contains a lot of logic around creating and editing projects and account management the rest of the overleaf stack along with information about contributing can be found in the overleaf overleaf https github com overleaf overleaf repository build process overleaf web uses grunt http gruntjs com to build its front end related assets image processing tasks are commented out in the gruntfile and the needed packages aren t presently in the project s package json if the images need to be processed again minified and sprited start by fetching the packages npm install grunt contrib imagemin grunt sprity then decomment the tasks in gruntfile coffee after this the tasks can be called explicitly via grunt imagemin and grunt sprity new docker based build process note that the grunt workflow from above should still work but we are transitioning to a docker based testing workflow which is documented below running the app the app runs natively using npm and node on the local system npm install npm run start ideally the app would run in docker like the tests below but with host networking not supported in os x we need to run it natively until all services are dockerised running tests to run all tests run make test to run both unit and acceptance tests for a module run make test module module overleaf integration unit tests the test suites run in docker unit tests can be run in the test unit container defined in docker compose tests yml the makefile contains a short cut to run these make test unit during development it is often useful to only run a subset of tests which can be configured with arguments to the mocha cli make test unit mocha grep authorizationmanager to run only the unit tests for a single module do make test unit module module overleaf integration module tests can also use a mocha grep argument make test unit module module overleaf integration mocha grep sso acceptance tests acceptance tests are run against a live service which runs in the acceptance test container defined in docker compose tests yml to run the tests out of the box the makefile defines make test acceptance however during development it is often useful to leave the service running for rapid iteration on the acceptance tests this can be done with make test acceptance app start service make test acceptance app run run as many times as needed during development make test acceptance app stop service make test acceptance just runs these three commands in sequence and then runs make test acceptance modules which performs the tests for each module in the modules directory note that there is not currently an equivalent to the start run x n stop series for modules during development it is often useful to only run a subset of tests which can be configured with arguments to the mocha cli make test acceptance run mocha grep authorizationmanager to run only the acceptance tests for a single module do make test acceptance module module overleaf integration module tests can also use a mocha grep argument make test acceptance module module overleaf integration mocha grep sso routes run bin routes to print out all routes in the project license and credits this project is licensed under the agplv3 license http www gnu org licenses agpl 3 0 html stylesheets overleaf is based on bootstrap http getbootstrap com which is licensed under the mit license http opensource org licenses mit all modifications less files in public stylesheets are also licensed under the mit license artwork silk icon set 1 3 we gratefully acknowledge mark james http www famfamfam com lab icons silk for releasing his silk icon set under the creative commons attribution 2 5 license some of these icons are used within overleaf inside the public img silk and public brand icons directories iconshock icons we gratefully acknowledge iconshock http www iconshock com for use of the icons in the public img iconshock directory found via findicons com http findicons com icon 498089 height id 526085 | sharelatex latex editor real-time javascript overleaf | front_end |
gcp-aoosicate-cloud-engineer | gpc aoosicate cloud engineer this is the tutorial from gcp associate cloud engineering https pplearn udemy com course google cloud certification associate cloud engineer learn lecture 25121396 content learning roadmap imgs xnip2023 05 22 09 24 09 jpg linux command bash 1 checking all running apps in linux https www 2daygeek com how to check all running services in linux service status all 2 start apache2 apt update apt install apache2 service apache2 stop service apache2 start br br br br br br br br tips 1 difference between instance template and machine image 1 instance template build template from raw 2 machine image use current running instance as image 3 https www youtube com watch v rvtjjd l6cs br br br br br br br br intro 1 materials link https www in28minutes com resources google cloud ace 2 without cloud high cost of procuring infrastructure needs ahead of time planning low infrasturcture utilization peak load provisioning dedicated infrastructure maintenance team 3 why cloud how about provisioning renting resources when you want them and releasing them back when you do not need them on demand resource provisioning aka elasticity 4 advantage of cloud trade capital expense for variable expense benefit from massive economies of scale stop guessing capacity stop spending money runnign and maintaining data centers go global in mins 5 learning path imgs imgs xnip2023 05 22 09 22 39 jpg br br br br br br br br 2 google cloud regions and zones 2 1 why we meed regions and zones deploy 1 dc in london 1 challenge 1 slow access for users from other parts of the world high latency 2 challenge 2 what if the data center crashes what if you application goes down low availability deploy 2 dc in london add one more data center 1 challenge 1 still there 2 challenge 2 solved 2 challenge 3 what if entire region of london is unavailable you app goes down multiple regions one more in mumbai 1 chall 1 partly solved deploy your app in other regions 2 chall 2 solved app still live from other dc 3 chall 3 solved served from mumbai 2 2 understand regions and zones of gcp regions 1 google provides 20 regions around the world 2 advantages high availability low latency global footprint adhere to government regulations zones 1 each region has 3 zones at least 2 increased availability and fault tolerance within same region 3 each zone has 1 or more discrete clusters cluster distinct physical infrastructure that is housed in a data center 4 zones in region are connected through low latency links 5 regions and zones example imgs imgs xnip2023 05 22 10 14 30 jpg br br br br br br br br 3 google compute engine for associate cloud engineer 3 1 google compute engine fundamentals gce 1 create and manage lifecycle of virtual machine vm instances 2 load balancing and auto scaling for multiple vm instances 3 attach storage to your vm instance 4 manage network connectivity and configuration for your vm instances 5 goal setup vm instances as http server distribute load with load balancers imgs imgs xnip2023 05 22 10 23 21 jpg 3 2 hands on 1 new vm instance 2 understand differnt types and images in gce genearl purpose e2 n2 n2d n1 best price performance ratio web and application servers small medium databases dev environments memory optimized m2 m1 ultra high memory workloads large in memory databases and in memory analytics compute optimized c2 compute intensive workloads gaming applications 3 e2 standard 2 e2 machine type family standard type of workload 2 of cpus 4 installing http webserver on gcp vm bash whoami pwd ls sudo su to update apt update install apache2 apt install apache2 go to the ip 10 176 12 23 imgs imgs xnip2023 06 14 17 37 23 jpg bash cd var www html echo hello world from hostname hostname u var www html index html 5 internal and external ip address external public ip addresses are internet addressable internal private ip addresses are internet to a corporate network bash stop apache2 service service apache2 stop start apache2 service service apache2 start 6 static ip use static ip address imgs imgs xnip2023 06 14 18 14 52 jpg static ip can be switched to another vm instance in same project 3 3 simplify web server setup 1 simplify bootstarpping install os patches or software when an vm instance is launched configure a startup script bash use y it will not ask question bin bash apt update apt y install apache2 echo hello world from hostname hostname i var www html index html imgs imgs xnip2023 06 14 18 38 13 jpg 2 creation with instance template instance templates define machine type image labels startup script provides a convenient way to create similar instances image family can be specified imgs imgs xnip2023 06 14 18 44 05 jpg create from template imgs imgs xnip2023 06 14 18 45 24 jpg br br br 3 reducing launch time with custom image installing os patches and software at launch of vm instances increases boot up time creating a custom image with os patches and software pre installed can be created from an instance a persistent disk a snapshot another image or a file in cloud storage can be shared across projects deprecate old images hardening an image customize images to your corporate security standards stop instance while create an image imgs imgs xnip2023 06 15 09 30 37 jpg created custom image imgs imgs xnip2023 06 15 09 40 56 jpg copy from instance templates and select our custom image imgs imgs xnip2023 06 15 09 42 49 jpg and we dont need install apache2 any more as image already contains that we need start it imgs imgs xnip2023 06 15 09 44 32 jpg br br br 4 troubleshooting apache br br br br br br br br 4 google compute optimizing costs and performance in gcp 4 1 save cost 1 sustained use discounts applicable for instances by gke and gce 2 committed use discounts commit for 1 year or 3 years up to 70 discount imgs imgs xnip2023 06 15 11 25 37 jpg 3 saving costs with preemptible vms short lived cheaper up to 80 compute instance can be stopped by gcp any time within 24 hrs instances get 30s warning use preempty vm s if you app are fault tolerant cost sensitive not immediate looks no more preemptible vms imgs imgs xnip2023 06 15 11 37 23 jpg 4 spot vms latest version of preempitble vms doesn t have maxinum runtime 5 billing billed by the second not billed for compute when a compute instance is stopped will be billed for any storage attached with it create budget to avoid amount imgs imgs xnip2023 06 15 13 51 32 jpg 6 live migration availability policy keep vm running when a host system needs to be updated live migration running instance is migrated to another host in the same zone important configuration on host maintenance migrate migrate vm instance to other hardware terminate stop the vm intance automatic restart imgs imgs xnip2023 06 15 14 01 17 jpg 7 custom machine type imgs imgs xnip2023 06 15 14 05 40 jpg 8 compute engine features gpus accelerate math intensive and graphics intensive workloads for ai ml add gpu high performance high cost use images with gpu libraries otherwise gpu will not be used gpu restrictions not supported all machine types on host maintenance can only have the value terminate vm instance general purpose n1 add gpu gpus 9 vm in gcp vm associated with a project can only change machine type after stop the instance filter imgs imgs xnip2023 06 15 14 21 32 jpg auto monitoring default cpu network bytes disk throughput iops cloud monitoring agent memory utilization disk space utilization 10 best practices chose zone and region based on cost regulations availability needs latency and specific hardware needs distribute instances in multiple zones choose right machine type reserve for committed use discounts for constant workloads use preemptible instances for fault tolerant use labels to indicate environment team business unit etc 11 scenarios pre requisites to be able to create a vm project billing acct compute engines api dedicated hardware for your compliance licensing and management needs sole tenant nodes imgs imgs xnip2023 06 15 14 50 03 jpg 1000s of vm to automate os patch management os inventory management and os configuration management vm manager imgs imgs xnip2023 06 15 14 55 36 jpg login to vm intance to install software ssh to the instance dont expose vm to internet dont assign external ip address want to allow http traffic to your vm configure firewall rules br br br br br br br br 5 gcloud for associate cloud engineer br br br 5 1 gcloud 1 command line interface to interact with google cloud resources 2 most gcp services supported 3 u can crud existing resources and perform actions like deployment 4 some gcp have specific cli tools cloud storage gsutil cloud bigquery bq cloud bigtable cbt kubernetes kubectl bash gcloud version imgs imgs xnip2023 06 15 15 54 05 jpg bash gcloud init br br br 5 2 gcloud config set bash gcloud config list gcloud config list account account patchapiuser pp devcos cyan iam gserviceaccount com gcloud config list compute region change default region or zone gcloud config set core project value gcloud config set compute region value gcloud config set compute zone value gcloud config set core verbosity value debug syntax gcloud config set section property value core compute sections project region zone properties specifying core is optional as it is default section gcloud config set project value gcloud config set verbosity value debug get more details with gcloud config set help gcloud config list help list all set or unset properties gcloud config list all opposite gcloud config unset br br br 5 3 managing multiple configs bash gcloud config configurations list create multiple configurations gcloud config configurations create my second configuration set property gcloud config set project pp devcos cyan gcloud config list gcloud config configurations list activate default configuration gcloud config configurations activate default describe a configuration gcloud config configurations describe my second configuration br br br 5 4 gcloud command structure playing with services gcloud group subgroup action group config or compute or container or dataflow or functions or iam or which service group are u playing with subgroup instances or images or instance templates or machine types or regions or zones which sub group of the services do u want to play with action create or list or start or stop or describe or what do u want to do bash create instance gcloud compute instances create my first instance from gcloud list all compute instances gcloud compute instances list delete instance gcloud compute instances delete my first instance from gcloud gcloud compute zones list gcloud compute regions list gcloud compute machine types list gcloud compute machine types list filter zone us central1 b gcloud compute machine types list filter zone us central1 b us central1 a us central1 c br br br 5 5 playing with gcloud compute instances create https cloud google com sdk gcloud reference compute instances create crating compute instances gcloud compute instances create name options machine type default type is n1 standard 1 gcloud compute machine types list custome cpus custom memory custom vm type n1 n2 custome machine custom cpu 6 custom memory 3072mb custom vm type n2 image or image family or source snapshot or source instance tempalte or source machine image beta service account or no service account zone us central1 b tags preemptible restart on failure default no restart on failure maintenance policy migrate default terminate boot disk size boot disk type boot disk auto delete default no boot disk auto delete deletion protection no deletion protection default metadata metadata from file startup script startup script url matadata from file startup script local path to script startup or metadata startup script echo hello world shutdown script network subnet network tier premium default standard accelerator type nvidia tesla v100 count 8 metadata install nvidia driver true gpu br br br 5 6 setting default region and zone for compute engine three options option 1 centralized configuration gcloud compute project info add metadata metadata google compute default region region google compute default zone zone imgs imgs xnip2023 06 20 09 52 52 jpg option 2 local glcoud configuration gcloud config set compute region region option 3 command specific zone or region in the command priority option 3 if exists overrides option 2 if exists overrides option 1 br br br 5 7 exploring gcloud command list and describe typically list commands are used to list a set of resources gcloud compute resources list gcloud compute images regions zones disk types list gcloud compute instances disks snapshots list most list commands support a few common options filter zone value gcloud compute zones list filter region us west2 sort by name name gcloud compute zones list sort by region gcloud compute zones list sort by region in reverse order uri gcloud compute zones list uri gcloud compute regions list uri gcloud compute imgages list sort by name filter project window cloud ubuntu os cloud typically describe commands are used to describe a specific resource gcloud compute images describe ubuntu 1604 xenial v20210203 project ubuntu os cloud gcloud compute regions describe us central1 br br br 5 8 playing with compute instances gcloud playing with compute instances gcloud compute instances list start stop delete reset describe move gcloud compute instances start example instance gcloud compute instances stop example instance 1 example instance 2 gcloud compute instances delete example instance delete disks value all or data or boot keep disks value all or data or boot gcloud compute instances move example instance 1 zone use central1 b destination zone us central1 f move a vm from one zone to another br br br 5 9 playing with instance template in gcloud gcloud compute instance templates create delete describe list gcloud compute intance templates create instance template script bash gcloud compute instance templates list gcloud compute instance templates create instance template from command line gcloud compute instance templates delete instance template from command line source instance source instance source instance zone which instance to create a template from supports almost all options supported by gcloud compute instances create name image or image family or source snapshot or source instance template service account or no service account tags preemptible restart on failure default no restart on failure maintenance policy migrate default terminate boot disk size boot disk type boot disk auto delete default no boot disk auto delete deletion protection no deletion protection default metadata metadata from file startup script startup script url network subnet network tier premium default standard accelerator type nvidia tesla v100 count 8 metadata install nvidia driver true gpu other examples gcloud compute intance templates delete instance template bash gcloud compute instances create my test vm source instance template my instance template with custom image | cloud |
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FreeRTOS_zynq | important read the file structure txt file to get an overview of the contents basic example of freertos in the microzedboard 1 create a hardware platform on xilinx sdk new application project a fill the form as next project name freertos generated target hardware select microzed hw platform pre defined boards support package select create new b leave the rest as default c click on next select empty application click on finish will generate a dummy application but it will also create the needed bsp and the hardware description 2 import the freertos in your workspace right click on project explorer import general existing project into workspace browse select the path from the given sources or downloaded from the git repo change referenced bsp right click on the new imported freertos project click on change referenced bsp select freertos generated bsp right click on the new imported freertos project run as gdb 3 see console logs optional right click on the new imported freertos project properties run debug settings freertos elf edit stdio connection check connect stdio to console port check to which port is your usb connected baud rate 115200 | os |
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stockchart | stockchart a full stack project done for freecodecamp backend certificate here is the url for the project https bunny stockchart herokuapp com user stories i can view a graph displaying the recent trend lines for each added stock i can add new stocks by their symbol name i can remove stocks i can see changes in real time when any other user adds or removes a stock for this you will need to use web sockets for deployment update the api url in custom js and input api key and db | server |
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travelappbackend | travelappbackend travel app backend | spring-boot java server-side api | server |
nlp-project | nlp project repository for the natural language processing course project team members anant kandikuppa b120519cs hemant pugaliya b120787cs pranay dhariwal b120762cs dependencies python version 2 7 numpy pip install u numpy scikit learn pip install u scikit learn contents data collection contains our chosen dataset stop word removal contains cleaned dataset without stopwords and the requisite python code for achieving the same vocabulary contains the vocabulary file and the code required to obtain it evaluate py code for performing 10 fold cross validation model p pickled model obtained by training our model on the entire dataset model2 p pickled model obtained by training our model on the entire dataset for part b model py contains the definition for the naivebayesclassifier class used to represent our unigram model model2 py contains the definition for the naivebayesclassifier class used to represent our bigram model train model py code to train and pickle a model unigram model and bigram model the naivebayesclassifier class defined in model py and model2 py is used to represent a unigram model and a bigram model respectively it provides the following methods init self default constructor train from file self data file trains a model instance using the data read from data file train self data labels trains a model using list of sentences as data and their corresponding labels as label test self test data returns a set of predicted labels corresponding to the list of sentences passed as test data pos word prob self word returns the conditional probability of a word being in the positive class neg word prob self word returns the conditional probability of a word being in the negative class classify self line returns the predicted class of a sentence passed as line write counts self line prints the counts of attributes for debugging purposes results for part a we obtained an accuracy of 91 9 after 10 fold cross validation of the model on our training data for part b we obtained an accuracy of 92 0 after 10 fold cross validation of the model on our training data | ai |
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alt the main components in mlsd width 100 height 100 a 3 linkedin feed ranking https rebrand ly mldesign a href https rebrand ly mldesign img src images feed png alt linkedin feed ranking width 100 height 100 a 4 ad click prediction https rebrand ly mldesign a href https rebrand ly mldesign img src images ads png alt ad click prediction width 100 height 100 a 5 estimate delivery time https rebrand ly mldesign a href https rebrand ly mldesign img src images delivery png alt estimate delivery time width 100 height 100 a 6 airbnb search ranking https rebrand ly mldesign a href https rebrand ly mldesign img src images air png alt airbnb search ranking width 100 height 100 a getting started how to resources list of promising companies wealthfront 2021 list https blog wealthfront com career launching companies list companies list prepare for interview common questions about machine learning interview process faqs md study guide study guide readme md contained minimum set of focus area to 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practice coding leetcode questions by categories for mle https mlengineer io common leetcode questions by categories 532b301130b advance topics read advance topics extra md study guide leetcode not all companies ask leetcode questions img src images lc png https docs google com spreadsheets d 1rcb1dvqclmtoglj5j nj5pic7tda n2u edit gid 274831950 note there are a lot of companies that do not ask leetcode questions there are many paths to become an mle you can create your own path if you feel like leetcoding is a waste of time i use lc time tracking https docs google com spreadsheets d 1rcb1dvqclmtoglj5j nj5pic7tda n2u edit gid 274831950 to keep track of how many times i solves a question and how long i spent each time once i finish non trivial medium lc questions 3 times i have absolutely no issues solving them in actual interviews sometimes within 8 10 minutes it makes a big difference a better way is to use leetplug chrome extension here https leetplug azurewebsites net static pages howto html leetcode questions by categories https mlengineer io common leetcode questions by categories 532b301130b sk cf77975462cb0c96e6a6daebaa3ab7b9 sql know sql join self join https www sqlservertutorial net sql server basics sql server self join inner left right etc use hackerrank https www hackerrank com domains sql to practice sql revise learn sql window functions window functions https www windowfunctions com questions intro programming java garbage collection https stackify com what is java garbage collection text java 20garbage 20collection 20is 20the machine 2c 20or 20jvm 20for 20short text the 20garbage 20collector 20finds 20these them 20to 20free 20up 20memory python pass by object reference https robertheaton com 2014 02 09 pythons pass by object reference as explained by philip k dick python gil fluent python chapter 17 http index of es varios 2 fluent 20python 20clear 20concise 20and 20effective 20programming pdf python multithread https realpython com intro to python threading python concurrency fluent python chapter 18 http index of es varios 2 fluent 20python 20clear 20concise 20and 20effective 20programming pdf statistics and probability the only cheatsheet that you ll ever need img src images stat cheatsheet png http www wzchen com probability cheatsheet learn bayesian and practice problems in bayesian https blogs kent ac uk jonw files 2015 04 puza2005 pdf let a and b be events on the same sample space with p a 0 6 and p b 0 7 can these two events be disjoint given that alice has 2 kids at least one of which is a girl what is the probability that both kids are girls credit swierdo https www reddit com user swierdo a group of 60 students is randomly split into 3 classes of equal size all partitions are equally likely jack and jill are two students belonging to that group what is the probability that jack and jill will end up in the same class given an unfair coin with the probability of heads not equal to 5 what algorithm could you use to create a list of random 1s and 0s big data not required for google facebook interview spark architecture http datastrophic io core concepts architecture and internals of apache spark and spark lessons learned https databricks com blog 2016 08 31 apache spark scale a 60 tb production use case html outdated since spark 3 0 release spark oom https stackoverflow com questions 21138751 spark java lang outofmemoryerror java heap space cassandra best practice https tech ebayinc com engineering cassandra data modeling best practices part 1 and here https cassandra apache org doc latest data modeling intro html link https towardsdatascience com when to use cassandra and when to steer clear 72b7f2cede76 image https user images githubusercontent com 1975237 109818900 5a25fc00 7be8 11eb 8715 ca7d279e84b6 png cassandra performance https www scnsoft com blog cassandra performance practice problem finding friends with mapreduce http stevekrenzel com finding friends with mapreduce everything in one page https mlengineer io big data knowledge for machine learning engineer interview 2020 148d7c335e12 source friends link sk 604c593c522db5195d3bda33dc4662d7 ml fundamentals collinearity https statisticsbyjim com regression multicollinearity in regression analysis and read more https www youtube com watch v cba9lj9ls8s features scaling https sebastianraschka com articles 2014 about feature scaling html random forest vs gbdt https medium com aravanshad gradient boosting versus random forest cfa3fa8f0d80 smote synthetic minority over sampling technique https arxiv org pdf 1106 1813 pdf compare discriminative vs generative model https medium com mlengineer generative and discriminative models af5637a66a3 and extra read http ai stanford edu ang papers nips01 discriminativegenerative pdf logistic regression https www youtube com watch v la3q9d7akq try to implement logistic regression from scratch bonus point for vectorized version in numpy completed in 20 minutes sample code from martinpella sample logistic regression ipynb followup with mapreduce version quantile regression https www youtube com watch v s203scty4xq t 954s l1 l2 intuition https www linkedin com pulse intuitive visual explanation differences between l1 l2 xiaoli chen decision tree and random forest fundamental https people csail mit edu dsontag courses ml16 slides lecture11 pdf explain boosting https web stanford edu hastie talks boost pdf least square as maximum likelihood estimator https www youtube com watch v gnu498s3o maximum likelihood estimator introduction https www youtube com watch v wflqtuovdik t 15s kmeans https stanford edu cpiech cs221 handouts kmeans html try to implement kmeans from scratch sample code from flothesof github io sample kmeans ipynb bonus point for vectorized version in numpy completed in 20 minutes follow up with worst case time complexity and improvement for initialization extra md fundamentals about pca http alexhwilliams info itsneuronalblog 2016 03 27 pca i didn t use flashcard https machinelearningflashcards com but i m sure it helps up to certain extend ab testing trustworthy online controlled experiments a practical guide to a b testing https www researchgate net publication 339914315 trustworthy online controlled experiments a practical guide to ab testing dl fundamentals the deep learning book https www deeplearningbook org read part ii https www deeplearningbook org contents part practical html machine learning yearning https d2wvfoqc9gyqzf cloudfront net content uploads 2018 09 ng mly01 13 pdf read from section 5 to section 27 neural network and backpropagation http cs231n stanford edu slides 2020 lecture 4 pdf activation functions https missinglink ai guides neural network concepts 7 types neural network activation functions right loss and optimization http cs231n stanford edu slides 2020 lecture 3 pdf convolution neural network notes https cs231n github io convolutional networks recurrent neural networks http cs231n stanford edu slides 2020 lecture 10 pdf ml system design ml classic paper technical debt in ml https papers nips cc paper 5656 hidden technical debt in machine learning systems pdf rules of ml https developers google com machine learning guides rules of ml an opinionated guide to ml research http joschu net blog opinionated guide ml research html there is valuable advice in the personal development section at the bottom ml productions scaling ml at uber https eng uber com scaling michelangelo dl in production https github com alirezadir production level deep learning food delivery uber eats trip optimization https eng uber com uber eats trip optimization uber food discovery https eng uber com uber eats query understanding personalized store feed https blog doordash com personalized store feed with vector embeddings 251ad7a2c09a doordash dispatch optimization https doordash engineering 2020 02 28 next generation optimization for dasher dispatch at doordash ml design common usecases ml system design primer https interview mlengineer io video recommendation https interview mlengineer io feed ranking https interview mlengineer io fraud detection tbd adtech ad click prediction trend https storage googleapis com pub tools public publication data pdf 41159 pdf ad clicks ctr https research fb com wp content uploads 2016 11 practical lessons from predicting clicks on ads at facebook pdf delayed feedbacks https blog twitter com engineering en us topics insights 2019 improving engagement on digital ads with delayed feedback html entity embedding https blog twitter com engineering en us topics insights 2018 embeddingsattwitter html star space embedding all the things https arxiv org pdf 1709 03856 pdf twitter timeline ranking https blog twitter com engineering en us topics insights 2017 using deep learning at scale in twitters timelines html recommendations instagram explore https ai facebook com blog powered by ai instagrams explore recommender system tiktok recommendation https newsroom tiktok com en us how tiktok recommends videos for you deep neural networks for youtube recommendations https storage googleapis com pub tools public publication data pdf 45530 pdf wide deep learning for recommender systems https arxiv org pdf 1606 07792 pdf 29 testimonials v amazon l5 ds i really found the quizzes very helpful for testing my ml understanding also the resources shared helped me a lot for revising concepts for my interview preparation this course will definitely help engineers crack machine learning engineering and data science interviews k facebook mle i really like what you ve built it ll help a lot of engineers d nvidia ds i have been using your github repo to prep for my interviews and got an offer with nvidia with their data science team thanks again for your help a booking woow this is very useful summaries so nice h microsoft that s incredible v intel the repo is extremely cohesive thanks again intro this repo is written based on real interview questions from big companies and the study materials are based on legit experts i e andrew ng yoshua bengio etc i have 6 yoe in machine learning and have interviewed more than dozen big companies this is the minimum viable study plan that covers all actual interview questions from facebook amazon apple google ms snapchat linkedin etc if you re interested to learn more about paid ml system design course click here course md this course will provide 6 7 practical usecases with proven solutions after this course you will be able to solve new problem with systematic approach acknowledgements and contributing 1 thanks for early feedbacks and contributions from vivian https github com liuvivian11 aragorn87 https github com aragorn87 and others you can create an issue or pull request on this repo you can also help upvote on producthunt https www producthunt com posts machine learning interview guideline 2 if you find this helpful you can sponsor this project it s cool if you don t 3 thanks to this community we have donated about 200 to hopeforpaws https www hopeforpaws org if you want to support you can contribute too on their website | interview-preparation deep-learning system-design mvp interivew machine-learning leetcode interview-questions | ai |
Ooui | ooui web framework img src https raw githubusercontent com praeclarum ooui master documentation icon png height 32 build status https github com praeclarum ooui actions workflows build yml badge svg https github com praeclarum ooui actions workflows build yml version package description nuget package https img shields io nuget v ooui svg https www nuget org packages ooui ooui https www nuget org packages ooui core library with html elements and a server nuget package https img shields io nuget v ooui aspnetcore svg https www nuget org packages ooui aspnetcore ooui aspnetcore https www nuget org packages ooui aspnetcore integration with asp net core nuget package https img shields io nuget v ooui forms svg https www nuget org packages ooui forms ooui forms https www nuget org packages ooui forms xamarin forms backend using ooui status documentation oouiformsstatus md nuget package https img shields io nuget v ooui wasm svg https www nuget org packages ooui wasm ooui wasm https www nuget org packages ooui wasm package your app into a web assembly ooui pronounced weee is a small cross platform ui library for net that uses web technologies it presents a classic object oriented ui api that controls a dumb browser with ooui you get the full power of your favorite net programming language plus the ability to interact with your app using any device try it online head on over to http ooui mecha parts http ooui mecha parts to tryout the samples you can also load https s3 amazonaws com praeclarum org wasm index html https s3 amazonaws com praeclarum org wasm index html to try the webassembly mode of ooui running xamarin forms that s xamarin forms running right in your browser try the samples locally bash git clone git github com praeclarum ooui git cd ooui dotnet run project samples samples csproj this will open the default starting page for the samples now point your browser at http localhost 8080 shared button http localhost 8080 shared button you should see a button that tracks the number of times it was clicked the source code for that button is shown in the example below example app here is the complete source code to a fully collaborative button clicking app csharp using system using ooui class program static void main string args create the ui var button new button click me add some logic to it var count 0 button click s e count button text clicked count times publishing makes an object available at a given url the user should be directed to http localhost 8080 shared button ui publish shared button button don t exit the app until someone hits return console readline make sure to add a reference to ooui before you start running bash dotnet add package ooui dotnet run with just that code a web server that serves the html and web socket logic necessary for an interactive button will start the many ways to ooui ooui has been broken up into several packages to increase the variety of ways that it can be used here are some combinations to help you decide which way is best for you table thead tr th ooui th th ooui aspnetcore th th ooui forms th th ooui wasm th th th tr thead tr td check td td td td td td td td a href https github com praeclarum ooui wiki web dom with the built in web server web dom with the built in web server a td tr tr td check td td check td td td td td td web dom with asp net core td tr tr td check td td check td td check td td td td xamarin forms with asp net core td tr tr td check td td td td check td td td td xamarin forms with the built in web server td tr tr td check td td td td td td check td td a href https github com praeclarum ooui wiki web dom with web assembly web dom with web assembly a td tr tr td check td td td td check td td check td td a href https github com praeclarum ooui wiki xamarin forms with web assembly xamarin forms with web assembly a td tr table how it works when the user requests a page the page will connect to the server using a web socket this socket is used to keep the server s in memory model of the ui the one you work with as a programmer in sync with the actual ui shown to the user in their browser this is done using a simple messaging protocol with json packets when the user clicks or otherwise interacts with the ui those events are sent back over the web socket so that your code can deal with them in the case of web assembly this same dataflow takes place however sockets are not used as all communication is done locally in the browser process contributing ooui is open source and i love merging prs please fork away and please obey the editorconfig file try to file issues for things that you want to work on before you start the work so that there s no duplicated effort if you just want to help out check out the issues and dive in | dotnet csharp ui cross-platform html websockets | front_end |
netket | div align center img src https www netket org logo logo simple jpg alt logo width 400 img div netket release https img shields io github release netket netket svg https github com netket netket releases anaconda server badge https anaconda org conda forge netket badges version svg https anaconda org conda forge netket paper v3 https img shields io badge paper 20 28v3 29 arxiv 3a2112 10526 b31b1b https scipost org scipostphyscodeb 7 pdf codecov https codecov io gh netket netket branch master graph badge svg token gzcolpo5lb https codecov io gh netket netket slack https img shields io badge slack chat green svg https join slack com t mlquantum shared invite zt 19wibmfdv llri6i43wrlev6oqx0ofow netket is an open source project delivering cutting edge methods for the study of many body quantum systems with artificial neural networks and machine learning techniques it is a python library built on jax https github com google jax homepage https www netket org citing https www netket org cite documentation https netket readthedocs io en latest index html tutorials https netket readthedocs io en latest tutorials gs ising html examples https github com netket netket tree master examples source code https github com netket netket installation and usage netket runs on macos and linux we recommend to install netket using pip but it can also be installed with conda it is often necessary to first update pip to a recent release 20 3 in order for upper compatibility bounds to be considered and avoid a broken installation for instructions on how to install the latest stable beta release of netket see the get started https www netket org get started page of our website or run the following command apple m1 users follow that link for more instructions sh pip install upgrade pip pip install upgrade netket if you wish to install the current development version of netket which is the master branch of this github repository together with the additional dependencies you can run the following command sh pip install upgrade pip pip install git https github com netket netket git egg netket all to speed up netket computations even on a single machine you can install the mpi related dependencies by using mpi between square brackets sh pip install upgrade pip pip install upgrade netket mpi we recommend to install netket with all it s extra dependencies which are documented below however if you do not have a working mpi compiler in your path this installation will most likely fail because it will attempt to install mpi4py which enables mpi support in netket the latest release of netket is always available on pypi and can be installed with pip netket is also available on conda forge however the version available through conda install can be slightly out of date compared to pypi to check what is the latest version released on both distributions you can inspect the badges at the top of this readme extra dependencies when installing netket with pip you can pass the following extra variants as square brakets you can install several of them by separating them with a comma dev installs development related dependencies such as black pytest and testing dependencies mpi installs mpi4py to enable multi process parallelism requires a working mpi compiler in your path extra installs tensorboardx to enable logging to tensorboard and openfermion to convert the qubitoperators all installs all extra dependencies mpi support to enable mpi support you must install mpi4jax https github com philipvinc mpi4jax please note that we advise to install mpi4jax with the same tool conda or pip with which you install it s dependency mpi4py to check whether mpi support is enabled check the flags python import netket netket utils mpi available true installation on windows warning windows support is experimental and you should expect suboptimal performance we suggest to use windows subsystem for linux wsl on which you can install netket following the same instructions as above and cuda and mpi work as intended however if you just want to quickly get started with netket it is also possible to install it natively on windows first download an unofficial build of jax from cloudhan jax windows builder https github com cloudhan jax windows builder sh pip install upgrade pip pip install jax cpu 0 3 25 f https whls blob core windows net unstable index html use deprecated legacy resolver alternatively you may specify a version with cuda support then install netket as usual sh pip install upgrade netket if you want mpi support please follow the discussion in mpi4jax https github com mpi4jax mpi4jax issues 24 getting started to get started with netket we recommend you give a look at our tutorials page https netket readthedocs io en latest tutorials gs ising html by running them on your computer or on google colaboratory https colab research google com there are also many example scripts that you can download run and edit that showcase some use cases of netket although they are not commented if you want to get in touch with us feel free to open an issue or a discussion here on github or to join the mlquantum slack group where several people involved with netket hang out to join the slack channel just accept this invitation https join slack com t mlquantum shared invite zt 19wibmfdv llri6i43wrlev6oqx0ofow license apache license 2 0 https github com netket netket blob master license | machine-learning-algorithms quantum neural-networks monte-carlo-methods hamiltonian physics-simulation variational-method variational-monte-carlo exact-diagonalization markov-chain-monte-carlo quantum-state-tomography complex-neural-network jax unitaryhack deep-learning machine-learning | ai |
cl-clone | craigslist clone w api live demo https cl dhcrain herokuapp com api documentation https cl dhcrain herokuapp com api docs create a craigslist clone from front to back learning objectives plan a full scale project translate high level feature requests into code image upload and display front end layout features login registration my account city selection page ability to store a city preference on user profile category and subcategory features main page index listing categories and sub categories implement a search feature posting a new classified posting list for any given subcategory must include 3 different data views list thumb gallery posting detail page should include images navigation sorting newest high price low price rest api features learning objectives adapt an existing large project to include api endpoints that mimic existing functionality and constraints base url https cl dhcrain herokuapp com api retrieve a list and detail of existing categories categories categories id retrieve a list and detail of existing sub categories sub categories sub categories id retrieve a list and detail of existing posts by sub category by category category listings id sub category listings id allow a user to create new categories and sub categories through the api but only if they have is superuser flag on their account retrieve a detail of a single post listings id create a new listing with all foreign key fields satisfied listings update an existing post include being able to change the sub category the post belongs to allow a user to create an account through the api and return the user their api access token register api token auth a non logged in user can never create or update any records a logged in user can never update a record created by another user use basic or token authentication to allow a user to authenticate | front_end |
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java-nlu-tools | java nlu tools java tools to do natural language processing like ner and intent classification on short sentences these tools provide an abstraction layer to mallet a machine learning for language toolkit http mallet cs umass edu common public license and apache opennlp https opennlp apache org apache license version 2 0 stanford corenlp is supported as well but due to their gpl license it is implemented as plugin https github com fquirin java nlu tools corenlp how to all training data can be stored in a common easy to read file with the following format how is the weather o o o o weather how s the weather in berlin o o o o location weather i need a taxi o o o o taxi service i need a cap to go to pier 39 o o o o o o o location location taxi service each line starts with a sentence in raw format followed by labels that are used to extract named entities and an intent connected to the sentence each part is separated by 3 dashes with space the default label for unnamed words tokens is o labels can be chosen as you like the more sentences you have the better but 15 per intent and named entity should be fine for testing to start training your model first choose your toolkit and extract the data to the required format import training data from compact custom format collection compactdataentry traindata customdatahandler importcompactdata compacttraindatafile declare a tokenizer for our model tokenizer tokenizer new reallifechattokenizer store train data for opennlp intent classification compactdatahandler cdh new opennlpdatahandler list string traindatalines cdh importtraindataintent traindata tokenizer null customdatahandler writetraindata trainfile traindatalines then you can start training start training with intent trainer intenttrainer trainer new opennlpintenttrainer null trainfilebase modelfilebase languagecode trainer train to test your model load it in a new classifier and call it like this intentclassifier ic new opennlpintentclassifier modelfilebase tokenizer languagecode collection intententry intentresults ic analyzesentence sentence intententry bestresult intententry getbestintent intentresults string bestintent bestresult getintent double bestcertainty bestresult getcertainty check out the examples https github com fquirin java nlu tools tree master src main java net b07z sepia nlu examples for each toolkit to get a complete overview of the export train test procedure | natural-language-processing conditional-random-fields maximum-entropy named-entity-recognition intent-classification | ai |
IEEE754-single_precision | floating point ieee 754 single precision this is my project for the embedded iot systems design course in this project an rtl multiplier has been created in verilog vhdl and systemc rtl it was then integrated into the com6502 splatters virtual platform and made in systemc tlm in various styles author enrico sgarbanti envq https github com envq esd lab univr for splatters virtual platform license this project is licensed under the gpl v3 license see the license md license md file for details | os |
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large_vlm_distillation_ood | distilling large vision language model with out of distribution generalizability iccv 2023 paper https arxiv org pdf 2307 03135 project poster https xuanlinli17 github io pdfs iccv23 large vlm distillation poster pdf large vision language models have achieved outstanding performance but their size and computational requirements make their deployment on resource constrained devices and time sensitive tasks impractical in this paper we investigate the distillation of visual representations in large teacher vision language models into lightweight student models using a small or mid scale dataset aiming to maintain the performance of teacher models notably this study focuses on open vocabulary out of distribution ood generalization a challenging problem that has been overlooked in previous model distillation literature we propose two principles from vision and language modality perspectives to enhance student s ood generalization 1 by better imitating teacher s visual representation space and carefully promoting better coherence in vision language alignment with the teacher 2 by enriching the teacher s language representations with informative and finegrained semantic attributes to effectively distinguish between different labels we propose several metrics and conduct extensive experiments to investigate their techniques the results demonstrate significant improvements in zero shot and few shot student performance on open vocabulary out of distribution classification teaser png teaser png distilling large vision language model with out of distribution generalizability distilling large vision language model with out of distribution generalizability setup setup preparing main datasets preparing main datasets running main experiments running main experiments running robotics experiments running robotics experiments citations citations license license setup first create an anaconda environment conda create n large vlm distillation ood python 3 8 then clone this repository and install it git clone https github com xuanlinli17 large vlm distillation ood cd path to this repo pip install e preparing main datasets the main datasets can be downloaded using the scripts in scripts download main data sh please refer to the file for more details and read the file before downloading the datasets for some datasets you need to manually click through their websites and download them after downloading the datasets for each dataset make a directory through mkdir dataset name then enter the directory through cd dataset name and extract the dataset files under this directory using tar xzvf path to dataset tar file then for each dataset split it into train in distribution validation val on train and out of distribution validation val sets cd path to this repo python scripts split dataset py data root the directory you extracted the dataset dataset name dataset name then for each dataset move the corresponding label2text txt and the chatgpt txt contained in this repo to the root path of dataset directory for root path ensure that there is a train folder and a val folder directly under it cd path to this repo cp data dataset name label2text txt dataset root path label2text txt cp data dataset name chatgpt txt dataset root path chatgpt txt optional if you wish to use ofa generated auxiliary captions for student training you can download the features from https drive google com drive folders 11gmlm8ramygr7q9glmiy9u3enyzrqlbp usp sharing and put them in the corresponding home dataset name train and home dataset name val directories if you d like to generate captions yourself please install ofa https github com ofa sys ofa first to successfully install ofa you might need to install an older setuptools package like pip install setuptools 59 5 0 after installing ofa put ofa gen captions py directly under the root directory of the ofa repo you can then enter the ofa repo and use ofa gen captions py to generate caption features note for the label2text txt and chatgpt txt files of tiered imagenet since tiered imagenet is a subset of imagenet we generated these files to cover the entire set of 1000 classes in the imagenet dataset so these files can be extended to imagenet as well running main experiments to train a student run the following command python main experiments py d path to dataset a student arch e g resnet18 or vit b 32 label path path to dataset s label2text txt repeat epochs repeat epochs schedule commands c save path batch args clip model args loss option args where for example for resnet students repeat epochs equals 5 for small datasets flowers cars birds and 1 for larger datasets food sun tiered imagenet for vit students repeat epochs equals 5 for small datasets and 3 for larger datasets schedule commands equals epochs 90 lr 0 05 schedule 30 60 gamma 0 1 for resnet students and epochs 90 repeat epochs 3 lr 0 0001 onecyclelr use adam for vit students batch args equals train batch 128 test batch 64 clip model args equals use clip clip model vit l 14 loss option args can be any number of the following options adding clip align image classification 1 enables l cls vision language alignment loss for classification setting clip align image classification 0 turns it off adding clip align image mse enables l mse which naively matches teacher visual features adding clip align image contrastive enables l cst adding clip align proximal text num 256 enables l vlalign with k 256 further adding clip filter out wrong alignment enables the filtering out of images misaligned with language labels in l vlalign adding chatgpt raw text file path to chatgpt txt enables chatgpt generated label descriptions adding clip align image aux caption enables auxiliary captions adding prompt learner enables prompt learning through coop to few shot finetune a student the commands are similar to the above except that replace schedule commands as epochs 20 lr 0 001 onecyclelr gamma 0 1 for resnet students and epochs 20 repeat epochs 3 lr 0 0002 onecyclelr use adam for vit students replace c as previous save path fewshot5 add these additional args few shot num 5 few shot method finetune add checkpoint to the args resume previous save path checkpoint pth tar a few more example commands are shown in scripts example main scripts sh running robotics experiments you can download the robotic dataset at https drive google com drive folders 1soukoz5ey0rbjq37hdiisqdgr5it2dh5 usp sharing example running scripts are in scripts example robotics scripts sh citations please cite our paper if you find our idea helpful thanks a lot inproceedings li 2023 iccv large vlm distillation author li xuanlin and fang yunhao and liu minghua and ling zhan and tu zhuowen and su hao title distilling large vision language model with out of distribution generalizability booktitle proceedings of the ieee cvf international conference on computer vision iccv month october year 2023 pages 2492 2503 license this project is licensed under the mit license | deep-learning few-shot-learning machine-learning open-vocabulary out-of-distribution out-of-distribution-generalization zero-shot-learning | ai |
machine-learning-book | machine learning with pytorch and scikit learn book code repository paperback 770 pages publisher packt publishing language english isbn 10 1801819319 isbn 13 978 1801819312 kindle asin b09nw48mr1 img src other cover 1 jpg width 248 https www amazon com machine learning pytorch scikit learn scikit learn ebook dp b09nw48mr1 dp b09nw48mr1 links amazon link https www amazon com machine learning pytorch scikit learn scikit learn ebook dp b09nw48mr1 dp b09nw48mr1 packt link https www packtpub com product machine learning with pytorch and scikit learn 9781801819312 blog post summarizing the contents https sebastianraschka com blog 2022 ml pytorch book html table of contents and code notebooks helpful installation and setup instructions can be found in the readme md file of chapter 1 ch01 readme md in addition zbynek bazanowski contributed this helpful guide supplementary running on colab pdf explaining how to run the code examples on google colab please note that these are just the code examples accompanying the book which we uploaded for your convenience be aware that these notebooks may not be useful without the formulae and descriptive text 1 machine learning giving computers the ability to learn from data open dir ch01 2 training machine learning algorithms for classification open dir ch02 3 a tour of machine learning classifiers using scikit learn open dir ch03 4 building good training sets data pre processing open dir ch04 5 compressing data via dimensionality reduction open dir ch05 6 learning best practices for model evaluation and hyperparameter optimization open dir ch06 7 combining different models for ensemble learning open dir ch07 8 applying machine learning to sentiment analysis open dir ch08 9 predicting continuous target variables with regression analysis open dir ch09 10 working with unlabeled data clustering analysis open dir ch10 11 implementing a multi layer artificial neural network from scratch open dir ch11 12 parallelizing neural network training with pytorch open dir ch12 13 going deeper the mechanics of pytorch open dir ch13 14 classifying images with deep convolutional neural networks open dir ch14 15 modeling sequential data using recurrent neural networks open dir ch15 16 transformers improving natural language processing with attention mechanisms open dir ch16 17 generative adversarial networks for synthesizing new data open dir ch17 18 graph neural networks for capturing dependencies in graph structured data open dir ch18 19 reinforcement learning for decision making in complex environments open dir ch19 br br sebastian raschka yuxi hayden liu and vahid mirjalili machine learning with pytorch and scikit learn packt publishing 2022 book mlbook2022 address birmingham uk author sebastian raschka and yuxi hayden liu and vahid mirjalili isbn 978 1801819312 publisher packt publishing title machine learning with pytorch and scikit learn year 2022 coding environment please see the ch01 readme md ch01 readme md file for setup recommendations translations into other languages serbian translation ma insko u enje uz pytorch i scikit learn https knjige kombib rs masinsko ucenje uz pytorch i scikit learn isbn 9788673105772 | machine-learning scikit-learn deep-learning neural-networks pytorch | ai |
QFramework | logo logo png https img shields io badge license mit blue svg https github com liangxiegame qframework blob master license qframework intro english readme en md qframework https github com liangxiegame qframework solid ddd mvc cqrs 1000 http processon com chart image 5c270aa6e4b007ba5d5029dc png https file liangxiegame com 6bf42306 0b2a 4417 bbcf 354af0132596 png http processon com chart image 5cbb1edce4b0bab90960a4f6 png qframework viewcontroller icontroller monobehaviour system model command event system isystem icontroller system model event event model imodel utility event utility iutility sdk api command system model event command icontroller isystem imodel command isystem imodel icontroller bindableproperty icontroller isystem imodel icommand command icontroller command 1 https github com haogehaojiu frameworkdesign unity 2018 4 x 2021 x qframework cs qframework cs qframework cs unitypackage qframework cs examples unitypackage qframework toolkits unitypackage qframework toolkits unitypackage qframework toolkitspro assetstore http u3d as sj9 qframework cs qframework qframework cs qframework cs qframework cs counterapp flappybird cubemaster shootingeditor2d unitypackage qframework cs examples unitypackage qframework toolkits qframework corekit uikit actionkit reskit packagekit audiokit qframework cs unitypackage qframework toolkits unitypackage qframework toolkits demo wuziqi qframework toolkits demo qframework toolkits unitypackage qframework toolkits demo wuziqi unitypackage qframework toolkits demo saolei qframework toolkits demo qframework toolkits unitypackage qframework toolkits demo saolei unitypackage qframework toolkitspro toolkits qframework toolkits assetstore http u3d as sj9 crazy car tastsong https github com tastsong qf github https github com tastsong crazycar qq 623597263 http shang qq com wpa qunwpa idkey 706b8eef0fff3fe4be9ce27c8702ad7d8cc1bceabe3b7c0430ec9559b3a9ce66 github issue github https github com liangxiegame qframework issues new gitee issue gitee https gitee com liangxiegame qframework issues qframework https learn u3d cn tutorial framework design 1 https github com haogehaojiu frameworkdesign 2 https github com haogehaojiu shootingeditor2d qf github gitee issue qq liangxiegame 163 com qf steam https store steampowered com app 1563700 taptap https www taptap cn app 208258 qf steam https store steampowered com app 2091640 hi eggplant qf steam https store steampowered com app 2149980 the first mountain qf p s qf cs taptap https www taptap com app 233228 qf steam https store steampowered com app 1517160 qf taptap https www taptap com app 201075 qframework cs b https space bilibili com 656352 roguelike qframework cs b https space bilibili com 60450548 channel collectiondetail sid 125221 playmaker qframework tookits b https space bilibili com 60450548 channel collectiondetail sid 919279 qframework qframework b https www bilibili com video bv1uu4y1i7wh qframework qframework gamepix https www gamepixedu com goods show 55 star stargazers over time https starchart cc liangxiegame qframework svg https starchart cc liangxiegame qframework gdtdftdqtd https github com gdtdftdqtd h3166179 https github com h3166179 wangedgar https github com wangedgar liangxiegame https github com liangxiegame unity et https github com egametang et et unity3d client and c server framework iframework onclick https github com onclick9927 iframework simple unity tools jengine https github com jasonxudeveloper jengine unity tinax framework https tinax corala space unity qcsharpstyleguide https github com liangxiegame qcsharpstyleguide donate asset store http u3d as sj9 5 give 5 star star star this repository jetbrains p a href https www jetbrains com from qframework img src https images gitee com uploads images 2020 0722 084147 cc1c0a4a 2253805 png alt jetbrains logo width 20 height 20 a p qframework https www gamepixedu com goods show 55 | unity unity3d unity2d unity-scripts frameworks qframework unity3d-framework unity-framework unity3d-plugin unity-asset u3d unity-3d unity-editor game-dev godot godot-addon godot-engine godot-plugin godot4 godotengine | os |
Awesome-web-dev | h1 align center awesome web dev projects h1 h3 align center this repository contains amazing and simple beginner friendly web development projects rocket h3 p align center you ll find two different project sections one is of 50 days 50 projects and other section contains cool web dev projects to help you upskill yourself p ul li a href 50days50projects 50 days 50 projects a li li a href dhruvi dhruvi s awesome projects a li ul h3 this site contains all the projects we built point right a href https ashish khanagwal github io awesome web dev awesome web dev projects a b under progress b h3 h4 want to improve the website want to collaborate on it first make sure to read a href https github com ashish khanagwal awesome web dev blob main contributing md contribution guidelines a h4 p please make sure to raise the a href https github com ashish khanagwal awesome web dev issues issues a first and wait to get it assigned to you before making pr p note have a look on already created issues try to work on them once your pr gets merged then don t forget to add your dev info in contributors https github com ashish khanagwal awesome web dev blob main contributors md file and we will give you a shout out on our socials h3 align center you ll find every project s working link here point down h3 div align center h1 id 50days50projects 50 days 50 projects h1 serial no project name live project link code base 1 expanding cards expanding cards https expandiing cards vercel app expanding cards https github com ashish khanagwal awesome web dev tree main day 1 expanding cards 2 progress bar progress bar https form progress bar vercel app progress bar https github com ashish khanagwal awesome web dev tree main day 2 progress bar 3 rotating navbar rotating navbar https rotating navbar vercel app rotating navbar https github com ashish khanagwal awesome web dev tree main day 3 rotating navbar 4 search bar search bar https search black vercel app search bar https github com ashish khanagwal awesome web dev tree main day 4 search bar 5 loading page loading page https loading page eta vercel app loading page https github com ashish khanagwal awesome web dev tree main day 5 loading page 6 scroll effect scroll effect https scroll effect vercel app scroll effect https github com ashish khanagwal awesome web dev tree main day 6 scroll effect 7 split page split page https split page vercel app split page https github com ashish khanagwal awesome web dev tree main day 7 split page 8 techn blogs webapp tech geeks https techgeeksblog netlify app tech geeks https github com geetika001 awesome web dev tree main technical 20blogs 20website 9 height converter height converter https 636524da483a130a61a91992 heightconverterweb netlify app height converter https github com drishti jain21 awesome web dev tree main drishti height 20converter 10 landing page landing page https travelllandingpage netlify app landing page https github com ashish khanagwal awesome web dev tree main bharat 20 landing 20page div h1 id dhruvi align center dhruvi s web dev projects h1 div align center serial no project name live project link code base 1 calculator calculator https calculator umber one vercel app calculator https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 calculator 2 swiggy slider swiggy slider https swiggy slider vercel app swiggy slider https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 swiggy s 20slider 3 carousel carousel https carousel teal vercel app carousel https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 carousel 4 form validation form validation https form validation eta sepia vercel app form validation https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 20form 20 20validation 20 5 sticky notes sticky notes https sticky notes six vercel app sticky notes https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 sticky 20notes 6 taj palace taj palace https palace seven vercel app taj palace https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 20 palace 20 7 music library music library https music library one vercel app music library https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project music 8 hotel website hotel website https template dusky rho vercel app hotel website https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 template 9 swiggy home page swiggy home page https 50 projects 50 days gnjg vercel app swiggy home page https github com ashish khanagwal awesome web dev tree main dhruvi s frontend project day 20 swiggy s 20home 20page div don t forget to give a star star to the repository | front_end |
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youi | youi build status https travis ci com outr youi svg branch master https travis ci com outr youi codacy badge https api codacy com project badge grade c0425ea823824cd7ab60659e8b9542dc https www codacy com app matthicks youi utm source github com amp utm medium referral amp utm content outr youi amp utm campaign badge grade codacy badge https api codacy com project badge coverage c0425ea823824cd7ab60659e8b9542dc https www codacy com app matthicks youi utm source github com utm medium referral utm content outr youi utm campaign badge coverage stories in ready https badge waffle io outr youi png label ready title ready https waffle io outr youi gitter https badges gitter im join 20chat svg https gitter im outr youi maven central https maven badges herokuapp com maven central io youi youi core 2 12 badge svg https maven badges herokuapp com maven central io youi youi core 2 12 latest version https index scala lang org outr youi youi core latest svg https index scala lang org outr youi javadocs https javadoc io badge io youi youi core 2 12 svg https javadoc io doc io youi youi core 2 12 next generation user interface and application development in scala and scala js for web mobile and desktop status there is heavy development going on toward 1 0 but youi releases are stable and used in several production systems modules youi is divided into modules of functionality to minimize the dependencies required for your specific usage app app unification of client and server to write complete applications scala and scala js canvas canvas user interface implementation on html canvas for greater power and flexibility than html provides client client http client for asynchronous request response and restful support scala communication communication communication framework to provide type safe communication between a client server scala and scala js core core core features generally useful for web and http scala and scala js dom dom features and functionality related to working with the browser s dom scala js example example example and test functionality for applications using youi hypertext hypertext extension functionality for working with html in a more powerful way macros macros internal macros for various internal uses optimizer optimizer html javascript and image optimizations to reduce extra overhead from your application server server base functionality for a web server scala server undertow serverundertow implementation of server server using undertow http undertow io scala spatial spatial spatial and math related functionality for matrix and other operations stream stream streaming functionality for on the fly processing and modification of any xml or html content scala utilities utilities internal utilities to support the infrastructure of youi external projects though this project has several sub modules where possible external projects exist to add optional functionality youi plugin https github com outr youi plugin an sbt plugin to simplify setting up your youi project youi template https github com outr youi template stand alone server instance to help designers work with html templates locally and support integration for developers youi designer https github com outr youi designer user interface designer tool to create edit import export and generate user interfaces for youi youi example https github com outr youi example an example project showing the basic usage of youi tutorials though youi provides many modules to accomplish many things the primary goal of youi is application development for web mobile and desktop take a look at the app app module for a great getting started tutorial examples more examples are located in the example directory run them with sbt examplejs fastoptjs examplejvm restart then load http localhost 8080 ui examples html or search with def path for urls roadmap https github com outr youi wiki roadmap | scala undertow communication dom ui scala-js mobile android ios desktop cross-platform web-application-framework websocket framework | front_end |
SQL_Database | background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 3 data analysis instructions data modeling data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes to primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title | sql python pandas data-analysis data-engineering | server |
iot-walkthrough | windows 10 iotcore walkthrough introduction this project s goal is to demonstrate guidelines for creating a windows 10 iotcore based product and walk through the creation of an iot device from implementation to final deployment the project has two applications one background application to receive sensor data and send it to the azure cloud receiving sensor data and analyzing it are important tasks in iot and a device will often operate in headless mode for monitoring thus we separate these tasks in an independent app it also receives application keys securely and saves user settings to azure one foreground application for user interaction this application shows local weather read by the background app information from the internet news and regional weather and interacts with the user playing media or showing a slideshow a settings page is also available to change settings app communication appcommunication png the applications are written using universal windows platform uwp thus the same foreground app can be run on both iot and desktop guides steps from implementation of apps to deployment are documented with an end to end solution each tutorial shows small code snippets and then links to the code running in the walkthrough project sections sections png 1 about the project software components softwarecomponents md wiring of weather shield to dragonboard 410c wiring readme md 2 background application installing iot templates and deploying a background app background installation readme md collecting sensor data through i2c background sensing readme md 3 foreground application creating a foreground application foreground creating readme md usage of text to speech speech texttospeech readme md receiving voice commands speech voicecommands readme md playing media foreground mediaplayer readme md 4 inter application communication creating an app service appservice creation readme md communication between applications appservice communication readme md showing local weather data appservice showingweatherdata readme md 5 connecting to the azure cloud connecting to an azure iot hub using the preconfigured remote monitoring solution azure iothubpreconfiguredsolution readme md saving application keys on azure azure devicetwin desiredproperties readme md synchronizing settings with azure azure devicetwin reportedproperties readme md 6 integration with third party services integration with openweathermap integrations openweathermap readme md integration with bing news integrations bingnews readme md associating the app with the windows store storedeployment readme md onedrive picture slideshow integrations onedrive readme md 7 preparing for deployment enabling secure boot bitlocker and configci security readme md saving azure keys to the tpm module and connecting with tokens security tpm readme md iot image creation 8 deployment creating a retail oem image this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | server |
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mini-blockchain | img src https drive google com uc export view id 1b2ar0mn8cv6ghcvg76tw0tz7a8lgzspk width 40 mini blockchain mini blockchain is a reference design for a blockchain system to demostate a full end2end flow in current blockchain technology there are so many open source projects of blockchain implementation like ethereum bitcoin eos etc but even running their code will take u couple of days to setup the goal of the project is to build a hello world version of blockchain to help people understand how the foundamental technology without wasting time reading in a huge code base to simulate the different roles like user miner booster and demostrate how they participate the blockchain network and it also simplify the deployment instead of configuring tons of library and debugging a version in a real distributed network it is single machine version to easy the debug test and iterate since it is primarily designed as a learning tool for people to learn the blockchain end to end it is meant to be as simple to understand as possible while still providing clear structure and workflow what is this not mini blockchain is not meant for real usage as it is a simplified version and has many hard coded parameters it s just meant for learning it also may just simply stop working at any time since there are some depandences what is included this reference system is written in go don t ask me why use go i just fucking lost my mind a main go to boost the blockchian and simnulate the workflow core to implement the blockchain role to implement different actors in the blockchain ecosystem test to implement some unit tests to keep the code in some quality even very little parts of the system this is how bitcoin mining works i follow the same flow except checking the 2016th block img src https drive google com uc export view id 1gzntnj7zsdgazcuxtarcnfhbkei2tgos width 60 some definitions user anyone who as an account hash address is a user user holds the coins in their account and is able to send receive coins to from other users transaction any coin transfer between two users is a transaction the activities of users will generate transaction which is the source of transaction pool miner a special role who doesn t generate transaction but collect validate transaction from the pool block a collection of validated transactions every miner can propose a block but only the one acknowledged by most of the miners will be the official block in the chian difficulty a measure of how difficult it is to find a hash below a given target valid blocks must have a hash below this target mining pools also have a pool specific share difficulty setting a lower limit for shares in bitcoin the network difficulty changes every 2016 blocks for my implementation the difficulty changes every block nonce a 32 bit 4 byte field to random the hash generation any change to the block data such as the nonce will make the block hash completely different the resulting hash has to be a value less than the current difficulty and so will have to have a certain number of leading zero bits to be less than that as this iterative calculation requires time and resources the presentation of the block with the correct nonce value constitutes proof of work reward when a block is discovered the miner may award themselves a certain number of bitcoins which is agreed upon by everyone in the network normally the rewarding transaction is the first transaction in a block proposed by the miner fee the miner is also awarded the fees paid by users sending transactions the fee is an incentive for the miner to include the transaction in their block in the future as the number of new bitcoins miners are allowed to create in each block dwindles the fees will make up a much more important percentage of mining income ethereum is a good example of fee usage how does the simulated workflow work first it will start to initialize an empty blockchain note for every blockchain project boosting it from beginning is the tricky part in this implementation i created a empty chian and then add a empty block as the head second it creates 1 miner to represent consortium blockchain if you don t know what is consortium blockchain read this https www blockchaindailynews com the difference between a private public consortium blockchain a24681 html this the miner will submit several empty blocks and earn rewards for each block third it creates 10 users and vest some coins for users to trade with each other the vesting is implemented by transferring the coins from miner to each user forth each user will start to randomly trade with each other and submit his transaction to the trasnactuon pool for mining fifty miner keeps mining validate transaction confirm block cool we got that great let s actually build this thing now building the code first you need to install golang if mac brew install go if ubuntu sudo apt get update sudo apt get y upgrade sudo apt get install y golang go once you have installed and rebooted log in then open up the program terminal now run the command sudo apt get update sudo apt get y upgrade sudo apt get y install git now you can clone the repository to get the full code cd git clone https github com codingtmd mini blockchain git do not forget to download the missing library go get d v go to the directory and build the code go build then run it with fun go run main go and you will see the console output as below workflow https drive google com uc export view id 1sdnbbreanwrk2dnqipctdwinm eei1vk if you need an ide normally i use microsoft visual studio code https code visualstudio com download microsoft visual studio code and go plugin https code visualstudio com docs languages go go plugin things to know the logging uses loggo plz check the configuration and usage here https github com juju loggo cool future work areas you can contribute todos use msg to communicate infro between miners users currently just function call same input tramnsaction merging add multi miner support a simple script to initialize a blockchain create a config for all hard code parameters better logging make a web ui to look into operation details like etherscan io create pos support create a wallet implementation and web ui add some animations that make it easier to understand build a dapp add ico simulation like how to vest coins to user reading list if you are not familiar with blockchain and its technology below info will help u to ramp up the knowledge articles vision https avc com 2018 05 is buying crypto assets investing https avc com 2018 05 is buying crypto assets investing https news earn com thoughts on tokens 436109aabcbe https news earn com thoughts on tokens 436109aabcbe https thecontrol co cryptoeconomics 101 e5c883e9a8ff https thecontrol co cryptoeconomics 101 e5c883e9a8ff https medium com cdixon why decentralization matters 5e3f79f7638e https medium com cdixon why decentralization matters 5e3f79f7638e https continuations com post 148098927445 crypto tokens and the coming age of protocol https continuations com post 148098927445 crypto tokens and the coming age of protocol https medium com cdixon crypto tokens a breakthrough in open network design e600975be2ef https medium com cdixon crypto tokens a breakthrough in open network design e600975be2ef https www theinformation com articles 14 ways the cryptocurrency market will change in 2018 https www theinformation com articles 14 ways the cryptocurrency market will change in 2018 https medium com fehrsam why decentralized exchange protocols matter 58fb5e08b320 https medium com fehrsam why decentralized exchange protocols matter 58fb5e08b320 https thecontrol co some blockchain reading 1d98ec6b2f39 https thecontrol co some blockchain reading 1d98ec6b2f39 app token v s protocol token https blog 0xproject com the difference between app coins and protocol tokens 7281a428348c https blog 0xproject com the difference between app coins and protocol tokens 7281a428348c https medium com blockchannel protocol tokens good for greedy investors bad for business 9002b40cf4cc https medium com blockchannel protocol tokens good for greedy investors bad for business 9002b40cf4cc https blog citowise com the basics coin vs token what is the difference 5cd270591538 https blog citowise com the basics coin vs token what is the difference 5cd270591538 baas platform https medium com acinq strike our stripe like api for lightning is live cd1dce76ce2e https medium com acinq strike our stripe like api for lightning is live cd1dce76ce2e https medium com jbackus blockchain platform plays 2827247a9014 https medium com jbackus blockchain platform plays 2827247a9014 https techcrunch com 2018 05 22 po et launches lab for developers to build apps on publishing blockchain https techcrunch com 2018 05 22 po et launches lab for developers to build apps on publishing blockchain business model https blog coinbase com app coins and the dawn of the decentralized business model 8b8c951e734 https blog coinbase com app coins and the dawn of the decentralized business model 8b8c951e734 consensus mechanism https www coindesk com blockchains feared 51 attack now becoming regular https www coindesk com blockchains feared 51 attack now becoming regular dapp https medium com fehrsam the dapp developer stack the blockchain industry barometer 8d55ec1c7d4 https medium com fehrsam the dapp developer stack the blockchain industry barometer 8d55ec1c7d4 decentralized exchange https medium com fehrsam why decentralized exchange protocols matter 58fb5e08b320 https medium com fehrsam why decentralized exchange protocols matter 58fb5e08b320 https www reuters com article crypto currencies coinbase coinbase acquires cryptocurrency trading platform paradex idusl2n1su1kk https www reuters com article crypto currencies coinbase coinbase acquires cryptocurrency trading platform paradex idusl2n1su1kk digital https medium com kinfoundation kin blockchain taking fate into our own hands f5bdfa759502 https medium com kinfoundation kin blockchain taking fate into our own hands f5bdfa759502 ens https medium com the ethereum name service a beginners guide to buying an ens domain 3ccac2bdc770 https medium com the ethereum name service a beginners guide to buying an ens domain 3ccac2bdc770 erc 20 https medium com james 3093 ethereum erc20 tokens explained 9f7f304055df https medium com james 3093 ethereum erc20 tokens explained 9f7f304055df https medium com 0xcert fungible vs non fungible tokens on the blockchain ab4b12e0181a https medium com 0xcert fungible vs non fungible tokens on the blockchain ab4b12e0181a https hackernoon com an overview of non fungible tokens 5f140c32a70a https hackernoon com an overview of non fungible tokens 5f140c32a70a fat protocol https www usv com blog fat protocols https www usv com blog fat protocols game https www usv com blog cryptokitties 1 https www usv com blog cryptokitties 1 https techcrunch com 2018 05 25 gravys new mobile game show is price is right mixed with qvc https techcrunch com 2018 05 25 gravys new mobile game show is price is right mixed with qvc http www businessinsider com fortnite esports prize pool money epic games 2018 5 http www businessinsider com fortnite esports prize pool money epic games 2018 5 login kit https medium com cleargraphinc introducing cleargraph 4713bc215a77 https medium com cleargraphinc introducing cleargraph 4713bc215a77 https tokensale civic com civictokensalewhitepaper pdf https tokensale civic com civictokensalewhitepaper pdf loyalty https medium com bitrewards why blockchain is a smart solution for loyalty programs 9443af408f71 https medium com bitrewards why blockchain is a smart solution for loyalty programs 9443af408f71 http www oliverwyman com our expertise insights 2017 mar blockchain will transform customer loyalty programs html http www oliverwyman com our expertise insights 2017 mar blockchain will transform customer loyalty programs html http www kaleidoinsights com analysis should blockchain power your customer loyalty program http www kaleidoinsights com analysis should blockchain power your customer loyalty program proof of work v s proof of stake https medium com robertgreenfieldiv explaining proof of stake f1eae6feb26f https medium com robertgreenfieldiv explaining proof of stake f1eae6feb26f recorded video https avc com 2017 12 video of the week token 1 0 vs token 2 0 https avc com 2017 12 video of the week token 1 0 vs token 2 0 https avc com 2017 12 video of the week the token summit conversation https avc com 2017 12 video of the week the token summit conversation research report https coincenter org report https coincenter org report rewarding system https techcrunch com 2018 05 22 tango card raises 35m for its rewards as a service gift card aggregation platform https techcrunch com 2018 05 22 tango card raises 35m for its rewards as a service gift card aggregation platform https medium com bitrewards why blockchain is a smart solution for loyalty programs 9443af408f71 https medium com bitrewards why blockchain is a smart solution for loyalty programs 9443af408f71 https techcrunch com 2018 05 11 hollywood producer plans to incentivise content viewers with tokens https techcrunch com 2018 05 11 hollywood producer plans to incentivise content viewers with tokens security token https medium com mkogan4 what the heck are tokenised securities 7cd1123cbdad https medium com mkogan4 what the heck are tokenised securities 7cd1123cbdad smart contract https medium com ninosm squashing bugs and stopping heists the coming arms race in smart contract infrastructure 9666fb830f65 https medium com ninosm squashing bugs and stopping heists the coming arms race in smart contract infrastructure 9666fb830f65 token https thecontrol co tokens tokens and more tokens d4b177fbb443 https thecontrol co tokens tokens and more tokens d4b177fbb443 some white papers bitcoin a peer to peer electronic cash system https bitcoin org bitcoin pdf ethereum a next generation smart contract and decentralized application platform https github com ethereum wiki wiki white paper ethereum a secure decentralised generalised transaction ledger https ethereum github io yellowpaper paper pdf beigepaper a ether tech spec https github com chronaeon beigepaper blob master beigepaper pdf enabling blockchain innovations with pegged sidechains https blockstream com sidechains pdf augur a decentralized open source platform for prediction markets https bravenewcoin com assets whitepapers augur a decentralized open source platform for prediction markets pdf the dai stablecoin system https github com makerdao docs blob master dai md sia simple decentralized storage http www sia tech sia pdf omniledger a secure scale out decentralized ledger via sharding https eprint iacr org 2017 406 pdf bitcoin utxo lifespan prediction http cs229 stanford edu proj2015 225 report pdf authors lei zhang https www linkedin com in codingtmd contact me leave me a message if you want to participate for fun | golang go blockchain-technology blockchain-demo hello-world blockchain-network blockchain mini-blockchain | blockchain |
db_practice | 2017ee94 db project ee436 database engineering lab project | server |
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IoT | iot local sensors | server |
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viky-ai | welcome to viky ai some natural language analysis examples illustration jpg raw true what s viky ai viky ai is a natural language processing platform it allows you to extract information from unstructured text contents the technical component nlp allows the extraction of structured information this extraction is defined within agents the agents are multilingual assistants to find relevant data the nlp component takes as input a set of agents in json format and unstructured textual content in order to provide a json stream of structured data as output the second technical component webapp webapp readme md is a web application that allows you to work collaboratively to set up agents by offering dedicated interfaces it also provides the interpret api in order to allow integration into a third party system getting started you can run viky ai on linux and macos requirements viky ai local install used for development relies on the following dependencies docker engine 19 and compose 1 24 ruby 2 6 and bundler 2 0 nodejs 10 and yarn 1 19 graphviz 2 40 imagemagick 6 9 postgresql client 11 setup and run 1 clone the repository with submodules using the following command git clone recurse submodules https github com viky ai viky ai git 2 setup the application using the following command cd webapp bin setup take a seat it may take a while setup can take up to 15 minutes and 3gb of disk space 3 within webapp directory start the application using the following commands foreman start the application is now available at the following address http localhost 3000 contributing we encourage you to contribute to viky ai please check out the contributing to viky ai guide contributing md everyone interacting in viky ai and its codebases issue trackers chat rooms and mailing lists is expected to follow this code of conduct code of conduct md license viky ai is released under the mit license licence txt | open-source natural-language-processing nlp natural-language-understanding rails c saas | ai |
automatic-reviewer-assignment | automatic reviewer assignment ml model to assign reviewers to your paper automatically backend setup it is recommended to use a virtual environment to set up the project you can use either venv or miniconda to create the virtual environment once the virtual environment is created install the requirements using the following commands by venv python3 m venv venv source venv bin activate by conda conda create n env name python 3 10 specify environment name and python version conda activate env name pip install r requirements txt some users might face a problem with the installation of the requirements especially psycopg 2 please refer to this issue https stackoverflow com questions 73088528 installing pycopg2 gave me an issue in ubuntu 22 4 pip3 version 22 2 to solve the problem for the postgres database install postgres on your system follow the instructions here https www postgresql org download create a user named postgres with password postgres in the postgres terminal in ubuntu this terminal can be accessed by typing sudo su postgres psql in the terminal create a database named nfp in postgres make a copy of the env example file and rename it to env if there is pg admin installed in the system it will be easy to administer and monitor the data using the platform run the development server uvicorn main app reload to run the website frontend getting started first run the development server bash npm run dev or yarn dev open http localhost 3000 http localhost 3000 with your browser to see the result we start with the pages index js the page auto updates as you edit the file the pages api directory is mapped to api files in this directory are treated as api routes https nextjs org docs api routes introduction instead of react pages we use tailwind css https tailwindcss com for styling the styles directory contains the global styles and the tailwind config js file contains the configuration for tailwind css workflow the workflow for the user interface can be understood as the user enters the website and is greeted with the home page the user can then log into their account or create a new account the user can then upload a paper pdf or enter the text of the abstract after which the user can search for authors journals or articles related to the particular abstract the user can then view the results of the search and select the authors journals or articles that they want | ai |
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HelloworldBlockchain | helloworld helloworld blockchain node helloworld blockchain admin node http localhost 8555 helloworld blockchain core helloworld blockchain dto dto helloworld blockchain model model helloworld blockchain node cd helloworld blockchain node mvn dmaven test skip true clean package install spring boot repackage assembly single cd target tar zxvf helloworld blockchain node tar gz cd helloworldblockchainnode start sh restart | blockchain |
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Woodpecker | woodpecker hallucination correction for mllms p align center img src assets name png width 88 height 88 p font size 7 div align center a href https 42976740ac53ddbe7d gradio live online demo paused due to insufficient gpus a a href https github com bradyfu woodpecker paper coming soon a div font p align center img src assets framework jpg width 96 height 96 p this is the first work to correct hallucination in multimodal large language models if you have any question please feel free to email bradyfu24 gmail com or add wechat id xjtupanda news 09 26 we release our code and the online demo the paper will be coming soon demo please feel free to try our online demo https 42976740ac53ddbe7d gradio live p align center img src assets example jpeg width 96 height 96 p preliminary 1 create conda environment bash conda create n corrector python 3 10 conda activate corrector pip r requirements txt 2 install required packages and models install spacy and relevant model packages following the instructions in link https github com explosion spacy this is used for some text processing operations bash pip install u spacy python m spacy download en core web lg python m spacy download en core web md python m spacy download en core web sm for our open set detector install groundingdino following the instructions in link https github com idea research groundingdino usage 1 inference to make corrections based on an image and a text output from mllm run the inference code as follows shell python inference py image path path to image text some text to be corrected detector config path to groundingdino swint ogc py detector model path to groundingdino swint ogc pth api key sk xxxxxxx the output text will be printed in the terminal and intermediate results saved by default as intermediate view json 2 demo setup we use mplug owl as our default mllm in experiments if you wish to replicate the online demo please clone the project https github com x plug mplug owl and modify the variables in https github com bradyfu woodpecker blob e3fcac307cc5ff5a3dc079d9a94b924ebcdc2531 gradio demo py l7 and https github com bradyfu woodpecker blob e3fcac307cc5ff5a3dc079d9a94b924ebcdc2531 gradio demo py l35 l36 then simply run bash cuda visible devices 0 1 python gradio demo py here we put the corrector components on gpu with id 0 and mplug owl on gpu with id 1 acknowledgement this repository benefits from mplug owl https github com x plug mplug owl groundingdino https github com idea research groundingdino blip 2 https huggingface co salesforce blip2 flan t5 xxl and llama adapter https github com opengvlab llama adapter thanks for their awesome works | hallucination hallucinations large-language-models llm mllm multimodal-large-language-models multimodality | ai |
doctor | br h1 align center doctors for web development h1 h2 align center h2 br p align center a href https doctor delta vercel app a p br terminal sh pnpm i pnpm 8 npm run build all cd examples web tools npm run test examples diy rules sh cd examples diy npm run test br web tools feature sh cd packages web tools npm run dev dev monorepo examples web tools npm run doctor webtools feature packages web tools src features ts packages web tools src features checkxxx ts export default api iapi api adddoctorwebtoolscheck async return label feature name description description enum off warn success error doctorlevel doctorlevel warn features index ts ts export default require resolve checkxxx doctor terminal packages sh mkdir doctor xxx cd doctor xxx npx create doctor preset packages web tools sh npm run dev debug monorepo examples windows windows 1 node 16 pnpm 8 2 sh rm rf shell git bash zsh 3 4 doctor pnpm i doctor doctors core monorepo br dumi ant design website 1 dumirc ts powered by and design 2 markdown powered by dumi sh npm run start | npm react ts vue | front_end |
DevaBul | devabul deva bul is a android mobile application that patients to exchange information in disease dedicated forums the application also will provide map benefits users can find their location and addresses of health institutions hospitals pharmacies etc used technologies html body h1 used technologies h1 java gradle mysql php volley library google apis maps places h1 changes to do for the right work of the project h1 1 in php files and other necessary mysql connections codes you have to write your webhost properties instead of x conn mysqli connect x x x x for example conn mysqli connect hostname username password database name br 2 in androidmanifest file android name com google android maps v2 api key android value x you have to write api code that take from https developers google com instead of x br 3 in activities you have to write path of your php file instead of x string url x loginstatuschange php for example string url www webhost com loginstatuschange php body html | android java mysql php volley google-api google-maps hospital location | server |
workshop-intro-computer-vision | introduction to computer vision workshop for introducing the field of computer vision to newcomers it focuses on machine learning approaches to solve the task of recognition starts with a general presentation of the topic and its applications and finishes with a python implementation of the simple k nn algorithm specialized in the classical iris dataset written in a jupyter notebook experiences ieee cs ufrn trainee program 10 04 2018 presented by vitor vin cius and artur as part of the trainee process for new members of the ieee computer society student chapter at ufrn | ai |
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zeus | zeus greenkeeper badge https badges greenkeeper io zcued zeus svg https greenkeeper io https travis ci org zcued zeus svg branch master https img shields io github license zcued zeus svg https img shields io badge prs welcome brightgreen svg getting started a simple and elegant component based ui library installation sh npm i zeus ui usage jsx import react from react import reactdom from react dom import provider button from zeus ui const app provider div h1 hello zeus ui h1 button click me button div provider reactdom render app document getelementbyid root click here https zcued github io zeus doc dist for more information feedback issues or feature requests can be created on our github page https github com zcued zeus issues browser support all browsers that react supports https reactjs org docs react dom html browser support are supported contributing before opening an issue or pull request please read the contributing guide https github com zcued zeus blob master contributing md | os |
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FreeRTOS_Learning_CN | freertos learning cn freertos | os |
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CHP019-Unity-step-by-step- | chp019 unity step by step unity in embedded system design and robotics a step by step guide | os |
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gps-design-systems-lwc | gps design systems lwc ci workflow https github com eschweitzer78 gps design systems lwc workflows ci badge svg https github com eschweitzer78 gps design systems lwc actions query workflow 3aci packaging workflow https github com eschweitzer78 gps design systems lwc workflows packaging badge svg https github com eschweitzer78 gps design systems lwc actions query workflow 3a 22packaging 22 codecov https codecov io gh eschweitzer78 gps design systems lwc branch main graph badge svg https codecov io gh eschweitzer78 gps design systems lwc a collection of salesforce lightning web components lwcs for selected governments design systems the collection covers salesforce experience cloud as well as salesforce communities for public sector solutions omnistudio and may be extended in the future to help with salesforce flows provided they re surfaced via an experience cloud community note that at this stage there is a dependency on omnistudio 242 11 at the minimum do read our change log changelog md if you plan on updating already installed packages in order to check for changes in behaviour or configuration changes structure sfgpsds is the folder for code that is reusable across individual design systems supported by this repo and sfdx project it must be installed as a first step sfgpsdsaunsw is the folder for code and assets pertaining to the design system of new south wales australia check the documentation web site https nswds dsforce dev sfgpsdsaunsws is the folder for code and assets pertaining to the specific design system of service nsw new south wales australia check the documentation website https nsws dsforce dev sfgpsdsauvic is the folder for code and assets to the design system of victoria australia pilot no production without prior consultation check the documentation website https vic dsforce dev sfgpsdsukgov is the folder for code and assets to the design system of the united kingdom early alpha no production installing beta versions using unlocked packages follow this set of instructions if you want to deploy the library in its most recent build to a more permanent environment than a scratch org or if you don t want to install the local developement tools you can use a non source tracked orgs such as a free developer edition org https developer salesforce com signup or a trailhead playground https trailhead salesforce com or one of your sandboxes 1 log in to your org 1 click a href https test salesforce com packaging installpackage apexp p0 04t5j000000lca7aae title sfgpsds this link a to install the sfgpsds unlocked package in your org 1 click a href https test salesforce com packaging installpackage apexp p0 04t5j000000lzudaam title sfgpsdsaunsw this link a to install the sfgpsdsaunsw unlocked package in your org 1 click a href https test salesforce com packaging installpackage apexp p0 04t5j000000lmgfaau title sfgpsdsaunsws this link a to install the sfgpsdsaunsws unlocked package in your org 1 click a href https test salesforce com packaging installpackage apexp p0 04t5j000000lceoaae title sfgpsdsauvic this link a to install the sfgpsdsauvic unlocked package in your org 1 click a href https test salesforce com packaging installpackage apexp p0 04t5j000000lke4aam title sfgpsdsukgov this link a to install the sfgpsdsukgov unlocked package in your org 1 click a href https test salesforce com packaging installpackage apexp p0 04t5j000000lke9aam title sfgpsdsukgovfull this link a to install the full sfgpsdsukgov unlocked package in your org including sfgpsds installing the production versions using unlocked packages kindly note that the packages might be the same as for non production orgs see above if the latest successful build has alreay been promoted for production make sure you fully understand the support support md and security security md implications of installing those assets in production the applicable license license md has also wide ranging disclaimer provisions 1 log in to your 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Nepali-NLP-Progress | nepali nlp progress this curated list is an attempt to organize research and available tools in nepali natural language processing this is by no means an exhaustive list so contributions are welcome character recognition devnet an efficient cnn architecture for handwritten devanagari character recognition guha et al nov 2019 https www researchgate net publication 337626194 devnet an efficient cnn architecture for handwritten devanagari character recognition optical character recognition system for nepali language using convnet sharma manish k and bhattarai bidhan 2017 https dl acm org doi 10 1145 3055635 3056635 dictionary based nepali word recognition using neural network dawadi et al 2017 https www ijser org researchpaper dictionary based nepali word recognition using neural network pdf nepali character recognition using deep belief nets neupane aadesh 2017 http aadeshnpn com wp content uploads 2018 09 nepali character recognition pdf improving nepali ocr performance by using hybrid recognition approaches pant nirajan bal b k jul 2016 https ieeexplore ieee org document 7785384 literature review of segmentation problems in nepali optical character recognition bal bal krishna and pant nirajan 2016 https www researchgate net publication 291348878 literature review of segmentation problems in nepali optical character recognition deep learning based large scale handwritten devanagari character recognition pant et al 2015 http ashokpant github io publications ashok 2015 deep pdf off line nepali handwritten character recognition using multilayer perceptron and radial basis function neural networks pant et al 2012 http ashokpant github io publications ashok 2012 off pdf research report on the nepali ocr bal b k rupakheti p sep 2009 https web archive org web 20150105025630 http www panl10n net english outputs 20phase 202 ccs nepal mpp papers 2009 research report nepali ocr pdf combining multiple feature extraction techniques for handwritten devnagari character recognition arora et al 2008 https arxiv org pdf 1005 4032 pdf classification improving nepali news classification using bidirectional encoder representation from transformers kafle et al nov 2022 https link springer com chapter 10 1007 978 981 19 1653 3 36 comparative analysis of nepali news classification using lstm bi lstm and transformer model wagle s s thapa s oct 2021 http conference ioe edu np ioegc10 papers ioegc 10 123 10159 pdf vector representation based on a supervised codebook for nepali documents classification sitaula et al mar 2021 https www researchgate net publication 349763908 vector representation based on a supervised codebook for nepali documents classification detecting clickbaits on nepali news using svm and rf dam et al mar 2021 http conference ioe edu np publications ioegc9 ioegc 9 018 90032 pdf an analysis of classification algorithms for nepali news acharya k and shakya s jul 2020 https www researchgate net publication 343228516 an analysis of classification algorithms for nepali news plagiarism detection framework using monte carlo based artificial neural network for nepali language bachchan r k timalsina a k oct 2018 https ieeexplore ieee org document 8586841 nepali sms filtering using decision trees neural network and support vector machine shahi t b shakya s oct 2018 https www researchgate net publication 334167706 nepali sms filtering using decision trees neural network and support vector machine nepali text document classification using deep neural network s subba n paudel and t shahi jun 2019 https www nepjol info index php tuj article view 28677 improving nepali news recommendation using classification based on lstm recurrent neural networks basnet a and timalsina a 2018 https ieeexplore ieee org abstract document 8586815 plagiarism detection framework using monte carlo based artificial neural network for nepali language bachchan r k timalsina a k 2018 https ieeexplore ieee org abstract document 8586841 automated news classification using n gram model and key features of nepali language dangol et al 2018 https www nepjol info index php scitech article view 23504 nepali news classification using na ve bayes support vector machines and neural networks shahi and pant 2018 https www researchgate net publication 324098346 nepali news classification using naive bayes support vector machines and neural networks nepali multi class text classification singh oyesh m 2018 https oya163 github io assets resume nepali text classification pdf trend analysis of technology news in nepali newspapers a case study the kathmandu post adhikari s and timalsina a 2017 http conference ioe edu np publications ioegc2017 ioegc 2017 61 pdf improving nepali document classification by neural network kafle et el 2016 http conference ioe edu np ioegc2016 papers ioegc 2016 42 pdf mobile sms spam filtering for nepali text using na ve bayesian and support vector machine shahi and yadav 2014 https www scirp org journal paperinformation aspx paperid 40857 a lexicon pool augmented naive bayes classifier for nepali text thakur and singh 2014 https www researchgate net publication 285835091 a lexicon pool augmented naive bayes classifier for nepali text clustering a comparative analysis of particle swarm optimization and k means algorithm for text clustering using nepali wordnet sarkar et al 2014 https www semanticscholar org paper a comparative analysis of particle swarm and for sarkar roy df68d6083221a6f93f688cd6dd4d9c781bad691f development of nepali character database for characterrecognition based on clustering neupane adesh 2014 https www academia edu 9953982 development of nepali character database for character recognition based on clustering semantic text clustering using enhanced vector space model using nepali language chiranjibi sitaula 2012 http gesj internet academy org ge download php id 1939 pdf co reference resolution a machine learning approach to anaphora resolution in nepali language senapati et al july 2020 https ieeexplore ieee org document 9200135 anaphoric resolution in nepali dev bahadur poudel and bivod aale magar corpus development ldc il the indian repository of resources for language technology choudhary narayan jan 2021 https link springer com article 10 1007 s10579 020 09523 3 construction and annotation of a corpus of contemporary nepali yadava et al 2008 https www researchgate net publication 228916687 construction and annotation of a corpus of contemporary nepali dependency parsing annotation projection based dependency parser development for nepali rai pooja chatterji sanjay dec 2022 https dl acm org doi 10 1145 3542696 a conceptual graph approach to the parsing of projective sentences pradhan et al 2019 http ijmcs future in tech net 15 1 r asiis pdf parsing in nepali language using linear programming problem a yajnik f bhutia and s borah 2019 https www researchgate net publication 327603883 parsing in nepali language using linear programming problem ic3 2018 parsing techniques using paninian framework on nepali language a yajnik and d sharma nov 2015 https www researchgate net publication 289366165 parsing techniques using paninian framework on nepali language report on nepali computational grammar rupakheti et al https www academia edu 2414578 report on nepali computational grammar embeddings npvec1 word embeddings for nepali construction and evaluation koirala p niraula n b aug 2021 https aclanthology org 2021 repl4nlp 1 18 pdf emotion recognition detecting emotion from short messages on nepal earthquake saharia navanath oct 2015 https ieeexplore ieee org abstract document 7343089 grammar argument structure of nepali verbs a study on lexico semantic ambiguity km manger 2018 https d1wqtxts1xzle7 cloudfront net 62366072 9 argument structure of nepali fully final20200314 11417 1vund97 pdf 1584196355 response content disposition inline 3b filename 3dargument structure of nepali verbs a stu pdf expires 1645040857 signature yqxr4f9jfs6y0wr9in hquee db 7erigd2w1qkruzb1ymavsegunwszg4vlo8a23jqan71zeo7yi4plvevsug33v0idlgmchbqelpdovdlwwlzv9xrew64phjvdivsefwzjk0eu1lhqszs10gjwiauhmca gmyp0zlf gtjvca4wepvyrph5venzytc5zmqqegz7axfpzyi 7eo2cw1kqplhtkkm0uj7fg buicsbyvkmum mebdb1rcv5orylpo gtrekbpd3ateld9xgllflw6yk2hftmfmlxyzh9uso zbq2ur9xv1sx2w rs35ie5uh lnpumx8pl5h8p0a key pair id apkajlohf5ggslrbv4za inflection and derivation in nepali noun adjective and adverb 1 inflection and derivation in nepali dr laxmi prasad khatiwada 2013 https www researchgate net publication 237202333 inflection and derivation in nepali noun adjective and adverb 1 inflection and derivation in nepali collation sequence in nepali pan localization gurung s khatiwada l p https web archive org web 20120916174715 http www panl10n net english final 20reports pdf 20files nepal nep01 pdf a collocation based approach to nepali postpositions hardie andrew jun 2008 https www researchgate net publication 240750540 a collocation based approach to nepali postpositions collocational properties of adpositions in nepali and english hardie andrew jun 2007 http ucrel lancs ac uk publications cl2007 paper 88 paper pdf book contemporary issues in nepalese linguistics yadava et al 2005 https www worldcat org title contemporary issues in nepalese linguistics oclc 70054532 architectural and system design of the nepali grammar checker b k bal p shrestha and m p pustakalaya https citeseerx ist psu edu viewdoc download doi 10 1 1 485 2921 rep rep1 type pdf structure of nepali grammar bal krishna bal 2004 https www researchgate net publication 237261579 structure of nepali grammar report on nepali computational grammar rupakheti et al https www researchgate net publication 237310273 report on nepali computational grammar book beyond preferred argument structure sentences pronouns and given referents in nepali genetti c crain l d sep 2003 https benjamins com catalog sidag 14 10gen benefactive constructions in nepali madhav p poudel 2000 http www lib kobe u ac jp repository 81001550 pdf aspects of nepali grammar santa barbara papers in linguistics volume 6 1994 dept of linguistics ucsb https www linguistics ucsb edu sites secure lsit ucsb edu ling d7 files sitefiles research papers working 20papers 20vol 206 pdf book a descriptive grammar of nepali and an analyzed corpus acharya jayaraj jun 1991 http press georgetown edu book languages descriptive grammar nepali and analyzed corpus image captioning nepali image captioning adhikari a and ghimire s nov 2019 https ieeexplore ieee org abstract document 8947436 language modeling nepberta nepali language model trained in a large corpus timilsina et al nov 2022 https aclanthology org 2022 aacl short 34 nepali encoder transformers an analysis of auto encoding transformer language models for nepali text classification maskey et al jun 2022 https aclanthology org 2022 sigul 1 14 comparative evaluation of transformer based nepali language models tamrakar s r silpasuwanchai c 2022 https assets researchsquare com files rs 2289743 v1 aa3f3ba4a38a880db3d6c5dc pdf c 1670229384 preprocessing of nepali news corpus for downstream tasks awale et al aug 2022 https www nepjol info index php lsnj article view 46553 the use of n gram language model in predicting nepali words khadka bal ram may 2022 https www nepjol info index php paj article view 45040 encoder decoder based nepali news headline generation mishra et al sep 2020 https www researchgate net profile jayshree rathi publication 344296357 encoder decoder based nepali news headline generation links 5f6461cba6fdcc0086297a25 encoder decoder based nepali news headline generation pdf cross lingual language model pretraining conneau a lample g 2019 https proceedings neurips cc paper 2019 file c04c19c2c2474dbf5f7ac4372c5b9af1 paper pdf lexicon development an experience in developing the nepali sense tagged corpus sarkar et al 2015 https ieeexplore ieee org document 7148388 some challenges of automated annotation in a multilingual scenario roy et al 2014 http www ijirset com upload 2014 december 67 some pdf a proposed nepali synset entry and extraction tool roy et al 2012 https research vu nl ws portalfiles portal 3628717 gwc2012 pdf page 317 extending corpus annotation of nepali advances in tokenisation and lemmatisation hardie et al 2011 https escholarship org content qt15t805x8 qt15t805x8 pdf t pfpo3r nepali lexicon khatiwada laxmi p and gurung s 2007 https www researchgate net publication 237202254 co author paper nepali lexicon nepali lexicon development bista et al https www researchgate net publication 267789368 nepali lexicon development linguistic code switching lince a centralized benchmark for linguistic code switching evaluation aguilar et al may 2020 https arxiv org abs 2005 04322 morphological analysis morph analyzer of verbs in nepali language bhutia et al 2021 https link springer com chapter 10 1007 2f978 981 15 6202 0 62 a vowel based word splitter to improve performance of existing nepali morphological analyzers on words borrowed from sanskrit adhikari m neupane a apr 2020 http old ku edu np kuset vol14 no1 adhikari and neupane vol 14 no 1 2020 pdf design of a morph analyzer for non declinable adjectives of nepali language borah et al 2017 https dl acm org doi 10 1145 3036290 3036307 development of a morph analyser for nepali noun token chhetri et al 2015 https www researchgate net publication 308821504 development of a morph analyser for nepali noun token building morphological analyzer for nepali rai r and bhat s m 2012 https www academia edu 5484848 building morphological analyzer for nepali a computational analysis of nepali morphology a model for natural language processing balaram prasain phd 2011 https ojs ub uni konstanz de jsal dissertations diss balaram pdf a morphological analyzer and a stemmer for nepali bal krishna bal https www researchgate net publication 237658531 a morphological analyzer and a stemmer for nepali a morphosyntactic categorisation scheme for the automated analysis of nepali hardie et al dec 2009 https www researchgate net publication 332863021 a morphosyntactic categorisation scheme for the automated analysis of nepali morphological analysis of verbs in nepali basnet s b and pandey s b 2009 https www academia edu download 15389403 nep ling 24 pdf page 28 nelralec bhasha sanchar working paper 2 categorisation for automated morphosyntactic analysis of nepali introducing the nelralec tagset nt 01 hardie et al 2005 https web archive org web 20120522194347 http www bhashasanchar org 80 pdfs nelralec wp tagset pdf named entity recognition named entity recognition for nepali data sets and algorithms niraula nobal chapagain jeevan may 2022 https journals flvc org flairs article view 130725 named entity recognition ner for nepali bal et al 2019 https link springer com chapter 10 1007 978 3 030 29750 3 6 named entity recognition for nepali language singh et al 2019 https arxiv org abs 1908 05828v1 named entity recognition for nepali text using support vector machines bam and shahi 2014 https www scirp org journal paperinformation aspx paperid 43828 named entity recognition for nepali language a semi hybrid approach dey et al 2014 https www ijeit com vol 203 issue 208 ijeit1412201402 04 pdf pos tagging probabilistic and neural network based pos tagging of ambiguous nepali text a comparative study pradhan a yajnik a feb 2021 https dl acm org doi abs 10 1145 3459104 3459146 fine grained part of speech tagging in nepali text shrestha i dhakal s s 2021 https www sciencedirect com science article pii s1877050921012230 nepali pos tagging using deep learning approaches sayami et al dec 2019 https easychair org publications preprint download lczp a deep learning approach for part of speech tagging in nepali language prabha et al 2018 https ieeexplore ieee org document 8554812 general regression neural network based pos tagging for nepali text archit yajnik apr 2018 https www researchgate net publication 325030165 general regression neural network based pos tagging for nepali text ann based pos tagging for nepali text archit yajnik 2018 https www semanticscholar org paper ann based pos tagging for nepali text archityajnik 8cc1a284d7ea51f267bb33dfdc520ad25d89a3f9 navid extracted part of speech tagging using statistical approach for nepali text archit yajnik 2017 https www semanticscholar org paper part of speech tagging using statistical approach yajnik 9f3a6aec785a7ba90999704cfecabb8c016a3d8e enhancing the performance of part of speech tagging of nepali language through hybrid approach sinha et al 2015 https www semanticscholar org paper enhancing the performance of part of speech tagging sinha veyie 415e970bee7291b7fe5679a3c7f845f446f899e7 hidden markov model based part of speech tagging for nepali language paul et al 2015 https ieeexplore ieee org document 7377332 support vector machines based part of speech tagging for nepali text shahi and dhamala 2013 https www semanticscholar org paper support vector machines based part of speech for shahi dhamala b36505276ef839d9b0cf193c6e65032ca5c73b37 hidden markov model based probabilistic part of speech tagging for nepali text jaishi m r jan 2009 representation learning exploring applications of representation learning in nepali nepal a yates a apr 2014 https link springer com chapter 10 1007 2f978 3 642 54906 9 12 semantic similarity semantic sentence similarity using finite state machine sitaula c and ojha y r 2013 https www scirp org journal paperinformation aspx paperid 40196 sentiment analysis multi channel cnn to classify nepali covid 19 related tweets using hybrid features sitaula c shahi t b mar 2022 https arxiv org abs 2203 10286 a hybrid feature extraction method for nepali covid 19 related tweets classification sitaula et al mar 2022 https www hindawi com journals cin 2022 5681574 deep learning based methods for sentiment analysis on nepali covid 19 related tweets sitaula et al nov 2021 https www hindawi com journals cin 2021 2158184 aspect based abusive sentiment detection in nepali social media texts singh et al dec 2020 https ieeexplore ieee org document 9381292 named entity based sentiment analysis of nepali news media texts bal et al dec 2020 https aclanthology org 2020 nlptea 1 16 aspect based sentiment analysis of nepali text using support vector machine and naive bayes tamrakar et al nov 2020 https www nepjol info index php tj article view 32824 twitter sentiment analysis during covid 19 outbreak in nepal pokharel b p jun 2020 https papers ssrn com sol3 papers cfm abstract id 3624719 sentiment analysis in nepali exploring machine learning and lexicon based approaches piryani rajesh et al jan 2020 https content iospress com articles journal of intelligent and fuzzy systems ifs179884 classifying sentiments in nepali subjective texts thapa et al 2016 https www researchgate net publication 311755938 classifying sentiments in nepali subjective texts detecting sentiment in nepali texts a bootstrap approach for sentiment analysis of texts in the nepali language gupta et al 2015 https www researchgate net publication 301403864 detecting sentiment in nepali texts a bootstrap approach for sentiment analysis of texts in the nepali language sentiment analysis on nepali movie reviews using machine learning a yadav and a k pant 2014 http ashokpant github io publications ashok 2014 sentiment pdf speech recognition large vocabulary continuous speech recognition for nepali language baral elina shrestha sagar dec 2020 http www ijsps com uploadfile 2020 1224 20201224035656984 pdf nepali speech recognition using cnn gru and ctc b bhatta b joshi and r maharjhan sep 2020 https www researchgate net publication 344402909 nepali speech recognition using cnn gru and ctc nepali speech recognition using rnn ctc model p regmi a dahal and b joshi jul 2019 https www researchgate net publication 334523238 nepali speech recognition using rnn ctc model crowd sourced speech corpora for javanese sundanese sinhala nepali and bangladeshi bengali kjartansson et al 2018 https storage googleapis com pub tools public publication data pdf c24ce05c8c8402a900ea26409230cef3d117910e pdf hmm based isolated word nepali speech recognition m k ssarma a gajurel a pokhrel and b joshi jul 2017 http mkslive com wp content uploads 2018 01 icmlc 4029 pdf spell checking nepali spell checker 1 1 and the thesaurus research and development bal krishna bal et al 2007 https web archive org web 20131020064957 http www panl10n net english final 20reports pdf 20files nepal nep05 pdf nepali spell checker bal krishna bal et al https web archive org web 20150105024511 http www panl10n net english final 20reports pdf 20files nepal nep04 pdf stemming a survey on various stemming techniques for hindi and nepali language upadhyaya et al aug 2021 https link springer com chapter 10 1007 978 981 16 2911 2 14 a novel rule based recursive stemming algorithm for nepali plagiarism detection shah et al 2020 https www academia edu 41677918 a novel rule based recursive stemming algorithm for nepali plagiarism detection a nepali rule based stemmer and its performance on different nlp applications koirala p shakya a 2020 https arxiv org abs 2002 09901 a new stemmer for nepali language shrestha and dhakal 2016 https ieeexplore ieee org document 7749008 reload true an affix removal stemmer for natural language text in nepali paul et al 2014 https www ijcaonline org archives volume91 number6 15882 3439 a hybrid algorithm for stemming of nepali text chiranjibi sitaula 2014 https file scirp org html 4 8701252 34712 htm subjectivity analysis analyzing facts and opinions in nepali subjective texts bal et al aug 2017 https ieeexplore ieee org document 8316445 summarization attention based recurrent neural network for nepali text summarization timalsina et al jun 2022 https www nepjol info index php jist article view 46709 extractive method for nepali text summarization using text ranking and lstm khanal et al oct 2021 https www researchgate net publication 360823578 extractive method for nepali text summarization using text ranking and lstm encoder decoder based nepali news headline generation mishra et al sep 2020 https www ijcaonline org archives volume175 number20 mishra 2020 ijca 920735 pdf salient sentence extraction of nepali online health news texts ranabhat et al 2019 http ictaes org wp content uploads 2019 07 4 21 26salient sentence extraction of nepali online health news texts pdf surveys natural language processing for nepali text a review shahi t b sitaula chiranjabi oct 2021 https link springer com article 10 1007 s10462 021 10093 1 survey of nlp resources in low resource languages nepali sindhi and konkani rajan annie salgaonkar ambuja jul 2021 https link springer com chapter 10 1007 978 981 16 0739 4 12 towards building advanced natural language applications an overview of the existing primary resources and applications in nepali bal krishna bal 2009 https www researchgate net publication 234790399 towards building advanced natural language applications an overview of the existing primary resources and applications in nepali text to speech nepali text to speech synthesis system using freetts shah et al 2018 https www nepjol info index php scitech article view 23498 building a natural sounding text to speech system for the nepali language research and development challenges and solutions bajracharya et al aug 2018 https pdfs semanticscholar org 39cd 441faacb00a318d7121e5bbee7d02e2f73c4 pdf ga 2 107574849 293167234 1590247508 1685822225 1585112028 enhancing the quality of nepali text to speech systems bal bal krishna and ghimire rupak raj aug 2017 https link springer com chapter 10 1007 978 3 319 65551 2 14 nepali text to speech using time domain pitch synchronous overlap add method malla p 2015 nepali text to speech synthesis system using esnola method of concatenation chettri b shah k b jan 2013 https citeseerx ist psu edu viewdoc download doi 10 1 1 303 4235 rep rep1 type pdf translation statistical and syllabification based model for nepali machine transliteration roy et al jul 2022 https link springer com chapter 10 1007 978 3 031 10766 5 2 low resource english to nepali sentence translation using rnn long short term memory with attention nemkul k shakya s mar 2021 https link springer com chapter 10 1007 2f978 981 33 4355 9 48 english to nepali sentence translation using recurrent neural network with attention nemkul k shakya s feb 2021 https ieeexplore ieee org document 9397185 efforts in the development of an augmented english nepali parallel corpus duwal s bal b k dec 2019 https lt4all elra info proceedings lt4all2019 pdf 2019 lt4all 1 94 pdf the flores evaluation datasets for low resource machine translation nepali english and sinhala english guzman et al 2019 https www aclweb org anthology d19 1632 neural machine translation hindi nepali laskar et al 2019 https aclanthology org w19 5427 pdf a comparative study of smt and nmt case study of english nepali language pair bal bal krishna and acharya praveen 2018 https www semanticscholar org paper tudy of smt and nmt 3a case s tudy of english nepali acharya bal 6bd57d32ec7e81e40705b3282da0305c52c94c17 p2df english to nepali statistical machine translation system paul et al 2018 https link springer com chapter 10 1007 978 981 10 6890 4 41 expansion of the first hindi nepali word net based bilingual dictionary and the advancement of the humanmachine interface chakraborty et al dec 2011 https research ijcaonline org iceice number6 iceice045 pdf development of a nepali english mt system using the apertium mt platform http ltk org np downloads report internship apertium mt 2011 pdf an approach towards the construction of the first hindi nepali word net based bi lingual dictionary and the challenges handled chakraborty et al 2011 experiences in building the nepali wordnet insights and challenges chakraborty et al 2009 https www semanticscholar org paper experiences in building the nepali wordnet insights chakrabarty 4b69af4ab75f8f7ee2de9deef383b6cc38f546bf rule based machine translation system in the context of nepali text to english text shrestha h k 2008 https cs ou edu gsa csrc08 abstracts hira abstract pdf handling honorification in dobhase online english to nepali machine translation system keshari et al 2007 https www researchgate net publication 269161645 handling honorification in dobhase online english to nepali machine translation system generation of interlinear form of nepali text with target language as english shrestha et al 2005 unl nepali deconverter keshari et al 2005 https www researchgate net publication 265043654 unl nepali deconverter transliteration removing language barrier a survey of machine transliteration k c h thapa s aug 2018 https www proquest com docview 2608094412 pq origsite gscholar fromopenview true word sense disambiguation nepali word sense disambiguation using variants of simplified lesk measure singh et al aug 2021 https link springer com chapter 10 1007 978 981 16 1681 5 4 word sense disambiguation using wsd specific wordnet of polysemy words dhungana et al sep 2014 https arxiv org abs 1409 3512 word sense disambiguation in nepali language dhungana u r shakya s may 2014 https ieeexplore ieee org document 6821655 word sense disambiguation using clue words dhungana u r shakya s 2014 https www nepjol info index php jie article view 10900 8874 knowledge based approaches to nepali word sense disambiguation rey et al 2014 http airccse org journal ijnlc papers 3314ijnlc05 pdf nepali wsd specific wordnet dhungana u r 2012 nepali word sense disambigution using adapted lesk algorithm dhungana u r 2011 resources for nepali word sense disambiguation shrestha et al oct 2008 https ieeexplore ieee org abstract document 4906758 word sense disambiguation a brief survey with application to nepali shrestha et al jan 2008 https www researchgate net publication 265154046 word sense disambiguation a brief survey with application to nepali application of nlp in health exploiting linguistic information from nepali transcripts for early detection of alzheimer s disease using natural language processing and machine learning techniques adhikari et al dec 2021 https www sciencedirect com science article abs pii s1071581921001798 detecting alzheimer s disease by exploiting linguistic information from nepali transcript thapa et al 2020 https link springer com chapter 10 1007 2f978 3 030 63820 7 20 miscellaneous linguistic taboos and euphemisms in nepali nobal b niraula and saurab dulal and diwa koirala 2020 https arxiv org abs 2007 13798v1 issues in encoding the writing of nepal s languages hall et al 2014 https link springer com chapter 10 1007 978 3 642 54906 9 5 research report on pda localization bal et al https web archive org web 20150105024515 http www panl10n net english final 20reports pdf 20files nepal nep19 pdf tools and resources pre trained embeddings 300 dimensional word embeddings for nepali language lamsal rabindra https ieee dataport org keywords nepali word embeddings fasttext embeddings https fasttext cc docs en crawl vectors html elmo embeddings and more for many south asian languages https www cfilt iitb ac in diptesh embeddings byte pair embeddings https bpemb h its org ne npvec1 https github com nowalab nepali word embeddings corpus nepali corpus with 3 2b tokens c4 multilingual https github com allenai allennlp discussions 5265 text summarization abstractive summary for bbc nepali news articles https github com csebuetnlp xl sum datasets speech audio from bible new testament along with transcription for nepali newari tharu and so on https www faithcomesbyhearing com audio bible resources recordings database more resources nepali nlp research papers https blog sushilawale com nlp nepali nepali nlp resources https github com rameshhpathak nepali nlp resources nepali nlp toolkit https github com sushil79g nepali nlp machine learning datasets for nepali researchers https github com amitness ml datasets contributing guidelines if there has been a mistake of any kind paper name link author attribution and so on or you want me to add new papers related to nepali nlp feel free to open an issue describing the case and i ll make sure to correct the mistake or add that paper in case you want to suggest changes or updates yourself then fork the repository and create a pull request | ai |
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altschool-cloud-exercises | altschool africa this repository is for all exercises given during the altschool cloud engineering program | cloud |
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mint | https cdn hashnode com res hashnode image upload v1527769914653 hy7bidakm jpeg mint mint is a tendermint based blockchain protocol that lets anyone build social apps easily mint was created out of a need for efficient data storage on blockchain it provides you with a simple boilerplate code for building social communities and gets out of your way quickly we have also released a front end client of the blockchain which is known as uphack http uphack co think of it as hackernews on blockchain this is one of the many experiments we have been doing at hashnode although it s super early we have released the codebase to get initial feedback from the community and improve further alpha software this is an alpha software and shouldn t be used in production you should use this software to experiment and learn more about blockchain more details this repo provides you with a boilerplate code for writing your own blockchain that stores json documents it s based on tendermint bft consensus https tendermint com you will most likely add some sort of consensus like pos proof of stake to this implementation for production deployment use this to build social apps that rely on data storage experimental so use with caution contribute a blockchain network needs validators we have deployed a demo app http uphack co with 4 validators of our own and we encourage you to become one and test it out at any time there will be 21 validators producing blocks remember there is no incentive to produce blocks yet you should become a validator only if you want to experiment and learn how blockchain networks work become a validator to become a validator please follow these steps step 1 email us sandeep hashnode com once we are ready we ll add you to a telegram channel where we ll collaborate step 2 spin off a new machine on your favorite cloud do aws google cloud etc it must be running on ubuntu and should have at least 4gb ram and 30gb disk space step 3 install tendermint check this guide https github com tendermint tendermint blob master docs install rst to get started step 4 install mongodb you will maintain the global state of the blockchain here step 5 install mint follow the steps below cd into your gopath src run git clone https github com hashnode mint this should clone the source code into mint directory install dep https github com golang dep a tool to manage dependencies in go run cd mint dep ensure run go install mint now mint should be available as a global binary note you may have to set gobin variable for go install to work step 6 now run tendermint init this should create a few config files inside tendermint config now run cat tendermint config genesis json and copy your generated public key you need to give it to us in our telegram channel all other validators will update their genesis json with your public key once everyone has updated their genesis json we ll give you a copy of it you need to ssh into your machine and replace tendermint config genesis json with the new copy now open up tendermint config config toml and fill out the following details moniker enter a name for your validator node seed paste 3f0d69a741e1cd399c5c2ca38d9f9711135e7a53 206 189 125 145 46656 0ee5713d18a6127dbeac10107860ef1c30edcfb9 192 241 232 63 46656 9446a039f0e2c1cd9a838bfb541f09e910a113ad 159 203 31 67 46656 tendermint will connect to these peers and start gossiping persistent peers paste the content of seeds tendermint will maintain persistent connections with these peers save the file and exit now run mint and tendermint to finish set up make sure mongodb is running at this point cd nohup mint mint log 2 1 nohup tendermint node consensus create empty blocks false tendermint log 2 1 nohup will make sure that the processes are running even after you have logged out of ssh session and closed the terminal the logs will be written to mint log and tendermint log now you should be able to produce blocks and take part in consensus if you want to be a non validating peer which means you don t want to take part in consensus you can do so by following the steps above step 2 onwards however the content of your genesis json will be different as it should contain the rest of the validators you can paste the following into your genesis json genesis time 2018 06 01t06 56 45 810497687z chain id mint test validators pub key type ac26791624de60 value 4blvmk pb9ogowu2qxsh54h emds2jlbmegsui3hsmg power 10 name pub key type ac26791624de60 value kuaknlaxxoqpvuja9o42hq4dah3lpwdetrgud7yb5ja power 10 name pub key type ac26791624de60 value t 1jn1k1vr7cwfnyu6p t2d4pylur3fsyijuqmhjeka power 10 name pub key type ac26791624de60 value osae1de oyfxvmok ndraq6exowxhylup iupyjmoga power 10 name app hash as soon as you run tendermint and mint you will start receiving blocks and the latest state will be saved in mongodb if you want to check the state open up mongo shell and use tendermintdb to explore the collections contributing code if you want to improve the code and want to offer feedback feel free to send a pr the whole purpose of open sourcing the repo at such an early stage is to get feedback and improve the code the front end for the blockchain is located at uphack co and the corresponding code is available here https github com hashnode uphack do you think your blockchain product needs mint let s talk shoot an email to one of the following emails sandeep panda sandeep hashnode com mint team mint hashnode com | blockchain golang abci tendermint tendermint-consensus tendermint-applications | blockchain |
wlox-frontend | wlox frontend please see main repository documentation at wlox wlox http www github com wlox wlox for instructions | front_end |
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FreeRTOS_cpp11 | c freertos gcc introduction included library implements an interface enabling c multithreading in freertos that is creating threads std thread std jthread locking std mutex std condition variable etc time std chrono std sleep for etc futures std assync std promise std future etc std notify all at thread exit c 20 semaphores latches barriers and atomic wait notify taking the advantage of custom integration with gcc this library provides api to set thread custom attributes like a stack size for example i have not tested all the features i know that thread local does not work it will compile but will not create thread unique storage this implementation is for gnu c compiler gcc only tested with gcc 11 3 and 10 2 for arm 32bit cmake generates eclipse project freertos 10 4 3 windows 10 qemu 6 1 0 although i have not tried any platforms other than arm and riscv i believe it should work the dependency is on freertos only if freertos runs on your target then i believe this library will too this library is not intended to be accessed directly from your application it is an interface between c and freertos your application should use the stl directly stl will use the provided library under the hood saying that none of the files should be included in your application s source files except two freertos time h to set system time and thread with attributes h to create threads with custom thread attributes e g stack size attached are example cmake projects one target is for nxp k64f cortex m4 microcontroller it can be built from the command line console cmake freertos cpp11 g eclipse cdt4 unix makefiles dk64frdmevk 1 cmake build another example lib test ca9 readme md is arm versatile express cortex a9 and is used to run the program in qemu instead of the physical hardware it can be build from the command line console cmake freertos cpp11 g eclipse cdt4 unix makefiles darmca9 1 cmake build background the c 11 standard introduced unified multithreading interface the standard defines the interface only it is up to the compiler vendors how to implement it multithreading requires a tasks sheduler running at low level which implicates an operating system is present both scheduler and os are beyond of the c standard definition obviously it is natural that the implementations from compiler vendors would cover most popular os s only like windows or linux what about embedded world microcontrollers and limited resources systems well there is so many embedded os s that it is certainly impossible to provide implementation for all of them should os vendor provide an implementation for different compilers maybe unfortumately c is not popular in embedded world vendors focus on plain c it is expected that when c compiler is used code will compile too nothing more is needed to deliver with the multithreading library is different there is an additional layer needed to interface os with c freertos is a small real time operarting system the core library is more like a task scheduler with few tools to synchronize access to resources it has few extension libraries like tcp ip stack and a file system this os is very popular in embedded world of small microcontrollers it is for free and delivered as a source code strong points of this small rtos are good performance small footprint and simple api although it is implemented in c there are many programmers that create their own api wrappers in c c language is not popular in embedded world i believe it is a mistake c has got everything what standard c has plus many nice features that make the code easier to express algorithm is safer and fast working with freertos is about managing resources mainly creating and releasing handles passing correct types as arguments etc i found that often instead of focusing on an algorithm i am checking for memory leaks or incorrect data types having code wrapped in c classes brings the development to a different level multithreading interface in c is very clean and simple to use on the negative side it is little bit heavy under the hood it might not be the best if an embedded application has to create and destroy new tasks often or control stack size and priorities c interface does not provide this features however if it is about starting a worker thread now and then or implementing a dispatch queue that is snoozing somewhere in the system waiting for tasks to be processed this interface will do the job finally not every embedded application is a hard real time application so how to make freertos working with the c multithreading interface hello world building a project is not much different than the regular way the project for arm is built it is not needed to understand how the library is implemented to use it the source code must not be accessed directly from the application it is called by gcc implementation itself that is user application uses components from std namespace only as usual freertos source and a startup code for a processor will be needed the following definitions should also be placed in freertosconfig h file define confignum thread local storage pointers 1 define pdms to ticks xtimeinms ticktype t ticktype t xtimeinms ticktype t configtick rate hz ticktype t 1000 ifndef pdticks to ms define pdticks to ms ticks long long ticks configtick rate hz 1000 endif and then the following files need to be included in the project condition variable h helper class to implement std condition variable critical section h helper class wrap freertos citical section it is for the internal use only freertos time cpp setting and reading system wall clock time freertos time h declaration freertos thread attributes h thread attributes definition thread with attributes h helper api to create std thread and std jthread with custom attributes thread gthread h helper class to integrate freertos with std thread thread cpp definitions required by std thread class gthr key cpp definition required by futures gthr key h declarations gthr key type h helper class for local thread storage bits gthr default h freertos gcc hook thread and mutex see below future cc taken as is from gcc code mutex cc taken as is from gcc code condition variable cc taken as is from gcc code libatomic c since gcc11 atomic is not included in gcc build for certain platforms need to provide it simple example application can be like that include condition variable include mutex include thread include queue include chrono int main std queue int q std mutex m std condition variable cv std this thread sleep for std chrono seconds 1 std thread processor std unique lock std mutex lock m while 1 cv wait lock q return q size 0 int i q front q pop lock unlock if i 0 return lock lock for int i 100 i 0 i m lock q push i m unlock cv notify one processor join gcc hook for the library to work the gcc must see the threading interface implementation the interesting file is the gthr h located in a gcc instalation directory this is what i have got in my arm distribution include c 8 2 1 arm none eabi arm v5te hard bits gthr h include c 8 2 1 arm none eabi arm v5te softfp bits gthr h include c 8 2 1 arm none eabi bits gthr h include c 8 2 1 arm none eabi thumb nofp bits gthr h include c 8 2 1 arm none eabi thumb v6 m nofp bits gthr h include c 8 2 1 arm none eabi thumb v7 nofp bits gthr h include c 8 2 1 arm none eabi thumb v7 fp hard bits gthr h include c 8 2 1 arm none eabi thumb v7 fp softfp bits gthr h include c 8 2 1 arm none eabi thumb v7 m nofp bits gthr h include c 8 2 1 arm none eabi thumb v7e m nofp bits gthr h include c 8 2 1 arm none eabi thumb v7e m dp hard bits gthr h include c 8 2 1 arm none eabi thumb v7e m dp softfp bits gthr h include c 8 2 1 arm none eabi thumb v7e m fp hard bits gthr h include c 8 2 1 arm none eabi thumb v7e m fp softfp bits gthr h include c 8 2 1 arm none eabi thumb v8 m base nofp bits gthr h include c 8 2 1 arm none eabi thumb v8 m main nofp bits gthr h include c 8 2 1 arm none eabi thumb v8 m main dp hard bits gthr h include c 8 2 1 arm none eabi thumb v8 m main dp softfp bits gthr h include c 8 2 1 arm none eabi thumb v8 m main fp hard bits gthr h include c 8 2 1 arm none eabi thumb v8 m main fp softfp bits gthr h each file is the same different directories are related to different arm cores for example cortexm4 should be linked with v7e m xx xx depending on floating point configuration the file itself has lots of code commented out this is an instruction for implementers it tells which functions must be implemented to provide multithreading in a system the end of the file looks like this ifndef glibcxx gthread use weak define glibcxx gthread use weak 1 endif endif include bits gthr default h ifndef glibcxx hide exports pragma gcc visibility pop endif the file includes a default implementation from gthr default h this file is in the same directory as gthr h what would be a default implementation for a system without a system yes empty functions so how to replace the default implementation with the one from the library this library has it s own gthr default h file with required code in it and stored exactly in the freertos cpp11 gcc bits directory this file is included only by exactly gthr h so as long as the compiler knows the path to cpp11 gcc it will also find the default implementation in the bits directory path to cpp11 gcc is given in the included cmake script library implementation mutex implementation of mutex is probably the simplest one because freertos api contains all the functions that almost directly translate to the gcc interface full implementation is in gthr freertos h here is just a sample typedef xsemaphorehandle gthread mutex t static inline void gthread mutex init function gthread mutex t mutex mutex xsemaphorecreatemutex static inline int gthread mutex destroy gthread mutex t mutex vsemaphoredelete mutex return 0 static inline int gthread mutex lock gthread mutex t mutex return xsemaphoretake mutex portmax delay pdtrue 0 1 static inline int gthread mutex unlock gthread mutex t mutex return xsemaphoregive mutex pdtrue 0 1 once these functions are defined it is possible to use all different variants of mutex from the std namespace e g unique mutex lock guard etc except timed mutex this one requires access to system time which will be described later in this article condition variable it is little bit tricky to implement a condition variable with freertos to match the std interface first of all it is good to understand what a condition variable is and how it is or should be implemented in a system good article is here https www microsoft com en us research wp content uploads 2004 12 implementingcvs pdf without going into much detail implementation is a collection of threads waiting for a condition that would let them to exit that waiting state it is a form of an event a thread is waiting for an event a module sends a notification and the thread wakes up the interface provides a function to notify just one thread or all of them freertos has few different ways of suspending and resuming a task thread the event groups looks promissing it maintains a list of waiting threads and wakes them all when an event has been notified however this interface seems does not provide a way to wake up a single task another one is direct to task notifications this one on the other hand requires an implementation handling a list of threads this method is less efficient but at least is possible to meet the std condition variable interface have a closer look at the std condition variable class the implementation is in condition variable header the snippet below is not a full class just an interesting part of it condition variable class condition variable typedef gthread cond t native type native type m cond public condition variable noexcept condition variable noexcept void notify one noexcept void notify all noexcept void wait unique lock mutex lock noexcept template typename predicate void wait unique lock mutex lock predicate p while p wait lock class has a single member variable m cond which is a handle to a native os interface freertos interface in this case there are also few member functions that have to be implemented by an external library that is a back door to provide operations on the native handle the wait with a predicate is implemented just shown here because it will be needed later to explain one detail two things are needed a queue of waiting tasks and a semaphore to synchronise the access to that queue both have to be stored in a single handle inside of the condition variable class the single handle is implemented as free rtos std cv task list class in the condition variable h file of this library it is a wrapper to std list and a freertos semaphore the semaphore class is in the same file class cv task list public using gthread t free rtos std gthr freertos using thrd type gthread t native task type using queue type std list thrd type cv task list default void remove thrd type thrd que remove thrd void push thrd type thrd que push back thrd void pop que pop front bool empty const return que empty cv task list lock que queue type unlock no copy and no move cv task list operator const cv task list r delete cv task list operator cv task list r delete cv task list cv task list delete cv task list const cv task list delete thrd type front return que front const thrd type front const return que front thrd type back return que back const thrd type back const return que back void lock sem lock void unlock sem unlock private queue type que semaphore sem once this class is defined the native handler needs to be defined too it is done in gthr freertos h together with mutexes typedef free rtos std cv task list gthread cond t now class std condition variable can see the cv task list class as a native handler great time for the missing functions the implementation is in condition variable cc file this file is part of gcc repository and is an interface to a native implementation in gthr default h file functions that need to be implemented are gthread cond wait gthread cond timedwait gthread cond signal gthread cond broadcast gthread cond destroy the gthread cond destroy has nothing to do and is empty the wait function is the one which keeps the secret of a condition variable snippet below it saves a handle of the current thread to the queue while the both mutexes are taken the first one is taken outside the wait call and is protecting the condition have a look at implementation of condition variable wait with a predicate this is important this is a contract that guarantees that only one thread is checking the condition at one time the second mutex protects the threads queue it makes sure that a different thread that calls notify one all does not modify the queue at the same time once the thread s handle has been pushed to the queue the thread is ready to suspend suspend might block the execution so the two mutexes must be unlocked and give a chance for other threads to execute the ultasknotifytake is a freertos function that will switch a task to a waiting state until the xtasknotifygive function is called it is worth making a comment that when the second unlock returns context can be switched it is possible that a different thread calls notify one all in that time in that case the task that has been pushed to the queue will be removed from that queue before even starting being suspended this is correct behaviour accordingly to the freertos documentation a call to ultasknotifytake will not suspend the task in that case regardles whether the task got suspended or not when ultasknotifytake returns it means that the xtasknotifygive has been called at least once that means the condition must be tested again and that means the mutex protecting the condition must be taken again however it could be that some other thread got access to the condition in the meantime so the immediate lock can lock the thread again next two functions broadcast and signal are almost the same both lock the access to the queue remove a task from the queue and wake that task difference is that signal wakes only one task and the broadcast wakes all of them in a loop static inline int gthread cond wait gthread cond t cond gthread mutex t mutex note mutex is taken before entering this function cond lock cond push gthread t native task handle cond unlock gthread mutex unlock mutex ultasknotifytake pdtrue portmax delay gthread mutex lock mutex lock and return return 0 static inline int gthread cond signal gthread cond t cond cond lock if cond empty auto t cond front cond pop xtasknotifygive t cond unlock return 0 static inline int gthread cond broadcast gthread cond t cond cond lock while cond empty auto t cond front cond pop xtasknotifygive t cond unlock return 0 the gthread cond timedwait has the same functionality as the wait version with a difference that a timeout in ms will be passed to the ultasknotifytake thread c 11 standard defines threading interface as in a snippet bellow important part to notice is that id is defined as part of the thread class namespace std class thread namespace this thread thread id get id noexcept void yield noexcept template class clock class duration void sleep until const chrono time point clock duration abs time template class rep class period void sleep for const chrono duration rep period rel time source cppreference com https en cppreference com w cpp header thread now have a look at thread header file in your gcc the file is quiet long so the snippet has only important parts class thread public abstract base class for types that wrap arbitrary functors to be invoked in the new thread of execution struct state virtual state virtual void m run 0 using state ptr unique ptr state typedef gthread t native handle type thread id class id native handle type m thread void join void detach returns a value that hints at the number of hardware thread contexts static unsigned int hardware concurrency noexcept private id m id void m start thread state ptr void the state class is used for passing a user thread function the native handle type is an underlying thread data holder type the code in my library must define exactly this type to hook to the gcc implementation easy to notice this is the same approach as in the condition variable interface the id is the place where the thread s handle is kept m thread and at last few functions of which definitions are missing thread state state thread hardware concurency thread join thread detach thread m start thread implementation is in thread cpp there is nothing special to do for the first two so namespace std thread state state default returns the number of concurrent threads supported by the implementation the value should be considered only a hint return value number of concurrent threads supported if the value is not well defined or not computable returns 0 unsigned int thread hardware concurrency noexcept return 0 not computable remember the gthr freertos h file the same one where the mutex interface is implemented this file has number of function definitions to support threads they can be used now to implement missing definitions of std thread class task s function is visible for the first time execute native thread routine this is an internal thread function user thread function is called inside definition will be described little bit later have a closer look at the m start thread interesting here is the state argument state ptr is a unique pointer t and is intended to keep user s thread function the raw pointer kept inside the unique pointer is passed to the native thread function this is important it means the ownership is passed now the native thread function is responsible for releasing it for that reason the thread function must execute the join would block by definition the detach must wait for the thread to start namespace std void thread m start thread state ptr state void const int err gthread create m id m thread execute native thread routine state get if err throw system error err state release both join and detach are simple one note on comparing threads in the typical implementation of these two functions m id s thread id type are compared directly however the overloaded compare operator makes copies of its arguments that is ok if the thread handle is just a pointer not so good if the handle is a class with few members so to optimise it the threads are compared directly instead less copies and assembler looks better as well void thread join id invalid if m id m thread invalid m thread gthread join m id m thread nullptr else throw system error einval destroy the handle explicitly next call to join detach will throw m id std move invalid void thread detach id invalid if m id m thread invalid m thread gthread detach m id m thread else throw system error einval destroy the handle explicitly next call to join detach will throw m id std move invalid so how is freertos attached to the thread handle two features of the rtos are needed a rtos task handle itself and an event group handle the join function must block until the thread function executes the events group matches this requirement perfectly as in the case of the condition variable both handles must be stored in one generic handle thread function vs std thread instance everything would be beautiful if not the detach function there is an issue that must be solved the generic handle keeps both handles just for the clarity of this explanation forget about the generic one for the moment so there are two handles a thread s handle and an event s handle the resources are allocated when a new thread starts when should the resources be released if the std thread instance exists as long as the thread executes then the destructor should be the right place however due to the detach function the thread execution can outlive the std thread instance should the handles be released in the thread function itself then what if the thread function finishes first the join function must have access to the event handle the handle must exist although it should be possible to check if the handle is valid i tried to go that way and i run into a race condition who gets first the thread function destroys the handle or join gets the handle because join must block on that handle then synchronisation becomes a challange i think simpler solution exists the solution is that the two handles have different life time the ugly part is that two handles must be kept in the same generic handle the rtos task handle is released at the end of the thread function the events handle is released at the end of join detach call here m id std move invalid the native thread function is here namespace std static void execute native thread routine void p gthread t local static cast gthread t p copy we own the arg now it must be deleted after run returns thread state ptr t static cast thread state local arg local notify started copy has been made tell we are running t m run if free rtos std s key free rtos std s key calldestructor gthread t self native task handle local notify joined finished release joined threads the handle is passed as a void pointer so casting is needed and at the same time a copy is made also the state is put back into the unique pointer t now it is time to notify that the thread has started execution and then call the user s task when the user s task function returns the state will be deleted by the scope and that means the thread has finished its function delete thread local data and notify the joined thread that is it native handle implementation the native thread handle is defined as gthread the definition comes from the gthr freertos h file typedef free rtos std gthr freertos gthread t the gthr freertos class is the generic handle the one that holds both handles inside the rtos task and the event handle the class is defined in thrad gthread h file and included in gthr freertos h class gthr freertos friend std thread enum eevstoragepos 0 estartedev 1 22 ejoinev 1 23 public typedef void task foo void typedef taskhandle t native task type gthr freertos const gthr freertos r gthr freertos gthr freertos r gthr freertos default bool create thread task foo foo void arg void join void detach void notify started void notify joined static gthr freertos self static native task type native task handle bool operator const gthr freertos r const bool operator const gthr freertos r const bool operator const gthr freertos r const void arg gthr freertos operator const gthr freertos r delete private gthr freertos default gthr freertos native task type thnd eventgrouphandle t ehnd gthr freertos operator gthr freertos r void move gthr freertos r void wait for start native task type taskhandle nullptr eventgrouphandle t evhandle nullptr void arg nullptr bool fowner false there is no point describing all of the functions below is a description in my opinion the most important ones critical section critical section is used in gthr freertos class functions this simple implementation is in reality disabling and enabling interrupts if this is not acceptable in your application implementation of this class should be changed namespace free rtos std struct critical section critical section taskenter critical critical section taskexit critical class is defined in critical section h file creating thread creating the freertos task requires allocating two handles they are not created in a constructor but in create thread function program will terminate if there is no resources alternatively the function could return false instead by default 512 words will be allocated for the stack this would be 2kb on arm standard c interface does not let define the stack size it is possible to configure the default stack size in words by setting the macro configdefault std thread stack size in your freertosconfig h file note that the change will apply to all threads the 2kb is required when futures are used without futures i had the system running with 1kb only this library allows for setting a custom attributes including a stack size for each thread critical section disables interrupts as described earlier the native thread function will delete thread s handle when finished so here critical section makes sure the thread does not start before the event s handle is stored in the thread s local storage bool gthr freertos create thread task foo foo void arg arg arg evhandle xeventgroupcreate if evhandle std terminate critical section critical auto attr internal attributes lock attrib xtaskcreate foo attr taskname attr stackwordcount this attr priority taskhandle if taskhandle std terminate vtasksetthreadlocalstoragepointer taskhandle eevstoragepos evhandle fowner true return true thread attributes sup 1 sup it is possible to create std thread and std jthread instances with custom attributes the thread with attributes h file provides api to create those threads there are two template functions std jthread to create std jthread and std thread to create std thread namespace free rtos std template typename args std thread std thread const free rtos std attributes attr args args free rtos std internal attributes lock lock attr return std thread std forward args args template typename args std jthread std jthread const free rtos std attributes attr args args free rtos std internal attributes lock lock attr return std jthread std forward args args the free rtos std attributes structure contains freertos task attributes that is task name task stack size task priority the way how it works is that there is a single global attributes instance initialized with default values when a std thread is created using c standard api those default attribute values are used when a thread with custom attributes is required the std thread function will create an instance of attributes lock which will swap the default values with the provided custom ones the attributes lock derives from critial section in that way the access to global attributes is thread safe when gthr freertos create thread is executed it creates a critical section in that time updating attributes is disabled scheduler is disabled and context switch will not happen on the other hand when the attributes lock is created it will prevent creating any other thread only this thread will use the custom attributes default values are restored when the attributes lock is destroyed join join waits for events to be notified by the native thread function the while loop makes sure it is not a spurious event there is no need to synchronise anything the thread function will not release the event handle even if the thread has finished execution void gthr freertos join while 0 xeventgroupwaitbits evhandle ejoinev estartedev pdfalse pdtrue portmax delay detach detaching will remove the event handle this can be done only if the thread has started execution functions std detach or std thread will destroy the handle native thread function must make a copy of this instance first to preserve the state pointer stored in arg event handle is stored in the task s local storage it must be set to an invalid handle now critical section is used to make sure that the task is not deleted while accessing the storage however task could not exist already it must be tested if it is the case so void gthr freertos detach wait for start critical section critical if edeleted etaskgetstate taskhandle vtasksetthreadlocalstoragepointer taskhandle eevstoragepos nullptr veventgroupdelete evhandle fowner false sending notifications both notifications are sent from the native thread functions first one is to tell that the thread has started and all necesarry copies have been made second notification is to tell that the user s thread function has finished and two threads can be joined now there is not much to do for start notifiation just setting a bit in the event group to notify the joining thread is more difficult there is a possibility that the thread has been detached and no one is waiting to join that means the event handle is deleted the event handle in this instance is just a copy and can point to a released memory in this case valid information is stored in the local storage if the handle is invalid this indicates the thread has been detached and it is safe to exit without sending a notification finally the task can be deleted freertos allows to pass nullptr as an argument to remove this task because the task is deleted function will not return for that reason the critical section is in its own scope it must be destroyed before deleting the task from that moment any task handle in any copies is invalid it can be tested using freertos api etaskgetstate function void gthr freertos notify started xeventgroupsetbits evhandle estartedev void notify joined critical section critical auto evhnd static cast eventgrouphandle t pvtaskgetthreadlocalstoragepointer taskhandle eevstoragepos if evhnd xeventgroupsetbits evhnd ejoinev vtaskdelete does not return vtaskdelete nullptr moving ownership std thread is passing the handle between functions quiet few times frequent copies are made the ownership is passed together with an ownership flag the code makes sure that the handles are destroyed only if the class is the owner gthr freertos class has a default destructor that does not touch the handles the handles are destroyed in the move operator it happens in the last line of the join detach functions gthr freertos gthr freertos const gthr freertos r critical section critical taskhandle r taskhandle evhandle r evhandle arg r arg fowner false gthr freertos gthr freertos operator gthr freertos r if this r return this taskenter critical if fowner if edeleted etaskgetstate taskhandle vtaskdelete taskhandle if evhandle veventgroupdelete evhandle fowner false else if r fowner taskexit critical r wait for start taskenter critical move std forward gthr freertos r taskexit critical return this futures i have to admit i have cheated to provide support for futures simply i just included files from gcc repository that is mutex cc and future cc copying files is not enough few extra functions must be implemented to make futures working once function std call once calls low level gthread once implementation is in gthr freertos h an external flag must be set to true when the function is called access to the flag is synchronised with a mutex function is not called when the flag has already been set static int gthread once gthread once t once void func void static gthread mutex t s m xsemaphorecreatemutex if s m return 12 posix error enomem gthread once t flag true xsemaphoretakerecursive s m portmax delay std swap once flag xsemaphoregiverecursive s m if flag false func return 0 at thread exit i found two functions in stl that require execution of user code after a thread has finished its execution these are std notify all at thread exit and family of functions std promise set value at thread exit i am not sure if there is more again gcc implementation is accesing functions in ghtr freertos h the calls are redirected to my implementation typedef free rtos std key gthread key t static int gthread key create gthread key t keyp void dtor void return free rtos std freertos gthread key create keyp dtor static int gthread key delete gthread key t key return free rtos std freertos gthread key delete key static void gthread getspecific gthread key t key return free rtos std freertos gthread getspecific key static int gthread setspecific gthread key t key const void ptr return free rtos std freertos gthread setspecific key ptr those functions provide a way of storing thread specific data to be honest i am not sure if my implementation does what is required i read posix description of those functions many times and i find it ambigous my understanding is that key create is called once in a thread function and it creates a single key then each thread running that function can store and load their specific data to that key so the key is a container of threads data associated with the thread handler in my code it is implemented as an unordered map also please notice the second argument of key create accordingly to posix description this is a destructor function that will be called when a thread has exited and the associated data is not null that key is defined in gthr key type h there is a map to store the data pointer to a destructor function and a mutex to synchronise the map struct key using gthread t free rtos std gthr freertos typedef void destructorfoo void key delete explicit key destructorfoo des desfoo des void calldestructor gthread t native task type task std mutex mtx destructorfoo desfoo std unordered map gthread t native task type const void specvalue then key creation is like namespace free rtos std key s key int freertos gthread key create key keyp void dtor void there is only one key for all threads if more keys are needed a list must be implemented assert s key s key new key dtor keyp s key return 0 storing and loading a value is just simple map manipulation functions are implemented in gthr key cpp last missing thing is how to hook it to thread destruction the key structure had a special function calldestructor function finds an associated thread specific data if found removes it from the storage and the previously registered destructor is called void calldestructor gthread t native task type task void val std lock guard lg mtx auto item specvalue find task if item specvalue end return val const cast void item second specvalue erase item if desfoo val desfoo val this function is called from std execute native thread routine in thread cpp right after the user thread function has returned namespace free rtos std extern key s key static void execute native thread routine void p at this stage t m run has finished execution if free rtos std s key free rtos std s key calldestructor gthread t self native task handle that is it from now on std promise std future etc will work thread local i could not make it work sad gcc for free standing systems bare metal no os is compiled with gthread active p function returning 0 my implementation returns 1 however gcc sees 0 most likely function got inlined during gcc build time zero indicates that a thread system is not active in that case a single instance of a variable is created insted of one per thread please let me know if there are other features that do not work system time last bit of c threading is sleep for and sleep until functions the first one is simple and requires just one function which is defined in thread cpp file it assumes that one tick in freertos is one millisecond time is converted to ticks and freertos api vtaskdelay does the job void this thread sleep for chrono seconds sec chrono nanoseconds nsec long ms nsec count 1 000 000 if sec count 0 ms 0 nsec count 0 ms 1 round up to 1 ms if sleep time 0 sleep at least 1ms vtaskdelay pdms to ticks chrono milliseconds sec count ms second function is in fact already implemented however it requires system time to operate sleep until calls gettimeofday which then calls gettimeofday this one must be implemented using freertos api in order to get time of day it would be nice to be able to set time of day first for this reason an additional function to set time is provided as far as i am aware ctime header does not provide a standard function for setting time my own implementation is provided instead both functions are in freertos time cpp file algorithm is very simple system ticks is a time counter then a global variable is needed to keep an offset between the real time and ticks the variable must be thread safe so namespace free rtos std class wall clock public struct time data timeval offset ticktype t ticks static time data time atomic critical section critical return time data timeoffset xtaskgettickcount static void time const timeval time atomic critical section critical timeoffset time private static timeval timeoffset timeval wall clock timeoffset setting time becomes easy just storing the difference between ticks and the time using namespace std chrono void setsystemclocktime const time point system clock system clock duration time auto delta time time point system clock milliseconds pdticks to ms xtaskgettickcount long long sec duration cast seconds delta count long usec duration cast microseconds delta count sec 1 000 000 narrowing type free rtos std wall clock time sec usec reading time is a reversed operation add the offset and ticks timeval operator const timeval l const timeval r extern c int gettimeofday timeval tv void tzvp void tzvp auto t free rtos std wall clock time long long ms pdticks to ms t ticks long long sec ms 1000 long usec ms sec 1000 1000 narrowing type tv t offset timeval sec usec return 0 return non zero for error summary there is few clever things in this library to manage hiding freertos behind generic handles but in general i believe it is a clean solution i have doubts about performance there is some copying involved also interrupts are disabled in few places however as i mentiond at the beggining not every embeeded application is a safety critical or hard real time one i could be wrong but i believe someone who wants a real time application would not use std thread in the first place anyway i believe that the main advantage of this library is the same generic c interface i find it handy to implement and debug certain algorithms in visual studio and then port it to a target board painlesly the thread local issue is dissapointing the only idea i have in my mind would be to fork gcc and recompile it with gthread active p returning 1 would it work would not it break the compiler i do not know give me a shout if you try my target was to make c multithreading available over freertos api so i did not bother to make posix c interface working for that reason i believe code in gthr freertos h would not compile in plain c project have not even tried it license files in this directory and subdirectories are covered with different licenses please have a look at license file in root directory for details 1 credit to jakub sosnovec for providing an initial solution to set custom stack size and inspiring me to extend the library with custom attributes | os |
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TNP-Database | tnp database training and placement cell database system for nitc as a software engineering project | server |
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on-emergence | are emergent abilities in large language models just in context learning cc by sa 4 0 cc by sa shield cc by sa abstract large language models have exhibited emergent abilities demonstrating exceptional performance across diverse tasks for which they were not explicitly trained including those that require complex reasoning abilities the emergence of such abilities carries profound implications for the future direction of research in nlp especially as the deployment of such models becomes more prevalent however one key challenge is that the evaluation of these abilities is often confounded by competencies that arise in models through alternative prompting techniques such as in context learning and instruction following which also emerge as the models are scaled up in this study we provide the first comprehensive examination of these emergent abilities while accounting for various potentially biasing factors that can influence the evaluation of models we conduct rigorous tests on a set of 18 models encompassing a parameter range from 60 million to 175 billion parameters across a comprehensive set of 22 tasks through an extensive series of over 1 000 experiments we provide compelling evidence that emergent abilities can primarily be ascribed to in context learning we find no evidence for the emergence of reasoning abilities thus providing valuable insights into the underlying mechanisms driving the observed abilities and thus alleviating safety concerns regarding their use contact persons sheng lu irina bigoulaeva harish tayyar madabushi https www ukp tu darmstadt de https www tu darmstadt de evaluation scores see evaluation scores https github com ukplab on emergence blob main evaluation scores csv for the evaluation scores of our experiments output files all of the output files associated to our experiments can be found here https tudatalib ulb tu darmstadt de handle tudatalib 3931 we name each file in a date time fashion e g results 20230524 123238 json which is also the run id of an experiment that can be found in evaluation scores https github com ukplab on emergence blob main evaluation scores csv citation please use the following citation misc lu2023emergent title are emergent abilities in large language models just in context learning author sheng lu and irina bigoulaeva and rachneet sachdeva and harish tayyar madabushi and iryna gurevych year 2023 eprint 2309 01809 archiveprefix arxiv primaryclass cs cl license this work is licensed under a creative commons attribution sharealike 4 0 international license cc by sa cc by sa 4 0 cc by sa image cc by sa cc by sa http creativecommons org licenses by sa 4 0 cc by sa image https licensebuttons net l by sa 4 0 88x31 png cc by sa shield https img shields io badge license cc 20by sa 204 0 lightgrey svg | ai |
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ml-powered-applications | building ml powered applications book cover images ml powered cover jpg welcome to the companion code repository for the o reilly book building ml powered applications http bit ly mlpowered oreilly the book is available on amazon http bit ly mlpowered this repository consists of three parts a set of jupyter notebooks in the notebook folder serve to illustrate concepts covered in the book a library in the ml editor folder contains core functions for the book s case study example a machine learning driven writing assistant a flask app demonstrates a simple way to serve results to users the images bmlpa figures folder contains reproductions of a few figures which were hard to read in the first print version credit and thanks go to bruno guisard https www linkedin com in bruno guisard who conducted a thorough review of the code in this repository setup instructions python environment this repository has been tested on python 3 6 and 3 7 it aims to support any python 3 version to setup start by cloning the repository git clone https github com hundredblocks ml powered applications git then navigate to the repository and create a python virtual environment using virtualenv https pypi org project virtualenv cd ml powered applications virtualenv ml editor you can then activate it by running source ml editor bin activate then install project requirements by using pip install r requirements txt the library uses a few models from spacy to download the small and large english model required to run the app and the notebooks run these commands from a terminal with your virtualenv activated python m spacy download en core web sm python m spacy download en core web lg finally the notebooks and library leverage the nltk package the package comes with a set of resources that need to be individually downloaded to do so open a python session in an activated virtual environment import nltk and download the required resource here is an example of how to do this for the punkt package from an active virtual environment with nltk installed python import nltk nltk download punkt notebook examples the notebook folder contains usage examples for concepts covered in the book most of the examples only use one of the subfolders in archive the one that contains data for writers stackexchange com i ve included a processed version of the data as a csv for convenience if you wanted to generate this data yourself or generate it for another subfolder you should download a subfolder from the stackoverflow archives archives run parse xml to csv to convert it to a dataframe run generate model text features to generate a dataframes with precomputed features archives https archive org details stackexchange the notebooks belong to a few categories of concepts described below data exploration and transformation dataset exploration datasetexploration splitting data splittingdata vectorizing text vectorizingtext clustering data clusteringdata tabular data vectorization tabulardatavectorization exploring data to generate features exploringdatatogeneratefeatures initial model training and performance analysis train simple model trainsimplemodel comparing data to predictions comparingdatatopredictions top k topk feature importance featureimportance black box explainer blackboxexplainer improving the model second model secondmodel third model thirdmodel model comparison comparing models comparingmodels generating suggestions from models generating recommendations generatingrecommendations blackboxexplainer notebooks black box explainer ipynb clusteringdata notebooks clustering data ipynb comparingdatatopredictions notebooks comparing data to predictions ipynb comparingmodels notebooks comparing models ipynb datasetexploration notebooks dataset exploration ipynb exploringdatatogeneratefeatures notebooks exploring data to generate features ipynb featureimportance notebooks feature importance ipynb generatingrecommendations notebooks generating recommendations ipynb secondmodel notebooks second model ipynb splittingdata notebooks splitting data ipynb tabulardatavectorization notebooks tabular data vectorization ipynb thirdmodel notebooks third model ipynb topk notebooks top k ipynb trainsimplemodel notebooks train simple model ipynb vectorizingtext notebooks vectorizing text ipynb pretrained models you can train and save models using the notebooks in the notebook folder for convenience i ve included three trained models and two vectorizers serialized in the models folder these models are loaded by notebooks demonstrating methods to compare model results as well as in the flask app running the prototype flask app to run the app simply navigate to the root of the repository and run flask app app py flask run the above command should spin up a local web app you can access at http 127 0 0 1 5000 troubleshooting if you have any questions or encounter any roadblocks please feel free to open an issue or email me at mlpoweredapplications gmail com project structure inspired by the great cookiecutter data science https drivendata github io cookiecutter data science | ai |
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self-attention-cv | div align center img src feat img png div self attention building blocks for computer vision applications in pytorch implementation of self attention mechanisms for computer vision in pytorch with einsum and einops focused on computer vision self attention modules install it via pip pip install self attention cv it would be nice to pre install pytorch in your environment in case you don t have a gpu to run the tests from the terminal pytest you may need to run export pythonpath pathonpath pwd before related articles how attention works in deep learning https theaisummer com attention how transformers work in deep learning and nlp https theaisummer com transformer how the vision transformer vit works in 10 minutes an image is worth 16x16 words https theaisummer com vision transformer understanding einsum for deep learning implement a transformer with multi head self attention from scratch https theaisummer com einsum attention how positional embeddings work in self attention https theaisummer com positional embeddings why multi head self attention works math intuitions and 10 1 hidden insights https theaisummer com self attention code examples multi head attention python import torch from self attention cv import multiheadselfattention model multiheadselfattention dim 64 x torch rand 16 10 64 batch tokens dim mask torch zeros 10 10 tokens x tokens mask 5 8 5 8 1 y model x mask axial attention python import torch from self attention cv import axialattentionblock model axialattentionblock in channels 256 dim 64 heads 8 x torch rand 1 256 64 64 batch tokens dim dim y model x vanilla transformer encoder python import torch from self attention cv import transformerencoder model transformerencoder dim 64 blocks 6 heads 8 x torch rand 16 10 64 batch tokens dim mask torch zeros 10 10 tokens x tokens mask 5 8 5 8 1 y model x mask vision transformer with without resnet50 backbone for image classification python import torch from self attention cv import vit resnet50vit model1 resnet50vit img dim 128 pretrained resnet false blocks 6 num classes 10 dim linear block 256 dim 256 or model2 vit img dim 256 in channels 3 patch dim 16 num classes 10 dim 512 x torch rand 2 3 256 256 y model2 x 2 10 a re implementation of unet with the vision transformer encoder python import torch from self attention cv transunet import transunet a torch rand 2 3 128 128 model transunet in channels 3 img dim 128 vit blocks 8 vit dim linear mhsa block 512 classes 5 y model a 2 5 128 128 bottleneck attention block python import torch from self attention cv bottleneck transformer import bottleneckblock inp torch rand 1 512 32 32 bottleneck block bottleneckblock in channels 512 fmap size 32 32 heads 4 out channels 1024 pooling true y bottleneck block inp position embeddings are also available 1d positional embeddings python import torch from self attention cv pos embeddings import absposemb1d relposemb1d model absposemb1d tokens 20 dim head 64 batch heads tokens dim head q torch rand 2 3 20 64 y1 model q model relposemb1d tokens 20 dim head 64 heads 3 q torch rand 2 3 20 64 y2 model q 2d positional embeddings python import torch from self attention cv pos embeddings import relposemb2d dim 32 spatial dim of the feat map model relposemb2d feat map size dim dim dim head 128 q torch rand 2 4 dim dim 128 y model q acknowledgments thanks to alex rogozhnikov arogozhnikov https github com arogozhnikov for the awesome einops package for my re implementations i have studied and borrowed code from many repositories of phil wang lucidrains https github com lucidrains by studying his code i have managed to grasp self attention discover nlp stuff that are never referred in the papers and learn from his clean coding style cited as article adaloglou2021transformer title transformers in computer vision author adaloglou nikolas journal https theaisummer com year 2021 howpublished https github com the ai summer self attention cv references 1 vaswani a shazeer n parmar n uszkoreit j jones l gomez a n polosukhin i 2017 attention is all you need arxiv preprint arxiv 1706 03762 2 wang h zhu y green b adam h yuille a chen l c 2020 august axial deeplab stand alone axial attention for panoptic segmentation in european conference on computer vision pp 108 126 springer cham 3 srinivas a lin t y parmar n shlens j abbeel p vaswani a 2021 bottleneck transformers for visual recognition arxiv preprint arxiv 2101 11605 4 dosovitskiy a beyer l kolesnikov a weissenborn d zhai x unterthiner t houlsby n 2020 an image is worth 16x16 words transformers for image recognition at scale arxiv preprint arxiv 2010 11929 5 ramachandran p parmar n vaswani a bello i levskaya a shlens j 2019 stand alone self attention in vision models arxiv preprint arxiv 1906 05909 6 chen j lu y yu q luo x adeli e wang y zhou y 2021 transunet transformers make strong encoders for medical image segmentation arxiv preprint arxiv 2102 04306 7 wang s li b khabsa m fang h ma h 2020 linformer self attention with linear complexity arxiv preprint arxiv 2006 04768 8 bertasius g wang h torresani l 2021 is space time attention all you need for video understanding arxiv preprint arxiv 2102 05095 9 shaw p uszkoreit j vaswani a 2018 self attention with relative position representations arxiv preprint arxiv 1803 02155 support if you really like this repository and find it useful please consider starring it so that it can reach a broader audience of like minded people it would be highly appreciated | deep-learning transformer transformers self-attention attention-mechanism attention machine-learning machine-learning-algorithms artificial-intelligence | ai |
iotjs | iot js platform for internet of things with javascript license https img shields io badge licence apache 202 0 brightgreen svg style flat license build status https travis ci org jerryscript project iotjs svg branch master https travis ci org jerryscript project iotjs coverity scan build status https scan coverity com projects 12140 badge svg https scan coverity com projects samsung iotjs sonarcloud status https sonarcloud io api project badges measure project pando project iotjs metric alert status https sonarcloud io dashboard id pando project iotjs fossa status https app fossa io api projects git 2bhttps 3a 2f 2fgithub com 2fsamsung 2fiotjs svg type shield https app fossa io projects git 2bhttps 3a 2f 2fgithub com 2fsamsung 2fiotjs ref badge shield irc channel https img shields io badge chat on 20freenode brightgreen svg https kiwiirc com client irc freenode net iotjs you can find project details on our project page http jerryscript project github io iotjs and wiki https github com jerryscript project iotjs wiki memory usage and binary footprint are measured at here https jerryscript project github io iotjs test results with real target daily the following table shows the latest results on the devices raspberry pi 3 remote testrunner https firebasestorage googleapis com v0 b jsremote testrunner appspot com o status 2fiotjs 2frpi3 svg alt media token 1 https jerryscript project github io iotjs test results view rpi3 raspberry pi 2 remote testrunner https firebasestorage googleapis com v0 b jsremote testrunner appspot com o status 2fiotjs 2frpi2 svg alt media token 1 https jerryscript project github io iotjs test results view rpi2 stm32f4 discovery remote testrunner https firebasestorage googleapis com v0 b jsremote testrunner appspot com o status 2fiotjs 2fstm32f4dis svg alt media token 1 https jerryscript project github io iotjs test results view stm32f4dis irc channel iotjs on freenode https freenode net mailing list iotjs dev groups io you can subscribe here https groups io g iotjs dev and access the mailing list archive here https groups io g iotjs dev topics quick start getting the sources bash git clone https github com jerryscript project iotjs git cd iotjs how to build bash tools build py how to test bash tools testrunner py build x86 64 linux debug bin iotjs trying out with a repl bash build x86 64 linux debug bin iotjs tools repl js for additional information see getting started docs getting started md documentation getting started docs getting started md api reference docs api iot js api reference md license iot js is open source software under the apache 2 0 license https www apache org licenses license 2 0 complete license and copyright information can be found within the code fossa status https app fossa io api projects git 2bhttps 3a 2f 2fgithub com 2fsamsung 2fiotjs svg type large https app fossa io projects git 2bhttps 3a 2f 2fgithub com 2fsamsung 2fiotjs ref badge large copyright 2015 present samsung electronics co ltd and other contributors licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license copyright node js contributors all rights reserved permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software this license applies to parts of js files in src js implementing node js compatible api originating from the https github com nodejs node repository | server |
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native.js | native js native js is a framework that aims to simplify cross platform mobile development through the use of javascript and an abstracting ui framework that relies on native ui elements because native js runs directly on top of a javascript engine and not in a browser it doesn t have the performance overhead that solutions like phonegap have moreover the framework uses native ui elements instead of those provided by a webview for familiar design and no compromise performance this is all made possible by the ui framework which binds the native ui elements of the respective platforms to a javascript api that is platform agnostic project status javascript api see the foundation of the ui framework api uiframeworkapi md android some basic functionality has been implemented so far i have started the android implementation of native js running on top of mozilla s rhino javascript engine another option was google s v8 javascript engine but i decided on rhino because v8 is a much larger library and it uses c which would require me to use the ndk while rhino is pure java see the framework status page for more details android ui framework status android uiframeworkstatus md ios the ios implementation has not been started yet | front_end |
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E-Store | e store this is an e commerce webpage that i made as a project in internshala web development training this is a simple e commerce webpage used to sell mobile phones technologies used are html css bootstrap php and sql | web-development e-commerce-project e-commerce-website internshala-finalproject internshala-project internshala-webdevelopment-project | front_end |
blockchain-demo | a simple implementation of blockchain this is a bare minimum implementation of the blockchain technology using typescript for educational purposes only configure the function inside the index ts to configure the blockchain instance this repository is part of a tutorial in medium https ankan101 medium com f2f8ccc54892 source friends link sk d92968f468d4e607355c6f0363e3be74 blockchain illustration https i imgur com wvnx1mq png getting started 1 install node js you need to have npm installed in your computer it comes with node js and you can get it by installing node from https nodejs org en 2 clone repository clone this repository from the terminal by running git clone https github com ankanbag101 blockchain demo 3 open the directory cd into the directory to run the program run cd blockchain demo 4 install dependencies run npm install to install all dependencies 5 run the program run npm start and the program will start executing with a usage instructions git clone https github com ankanbag101 blockchain demo cd blockchain demo npm install npm start usage 1 to add a block to the blockchain enter add sender name receiver name transfer amout in the prompt br example add bob alice 200 will create create a block with the transaction of amount 200 sent by bob to alice 2 to show all the transactions in the blockchain enter show in the prompt to show last 10 verified transactions in the blockchain 3 to tamper with a transaction enter tamper with a block number shown in the show command br example tamper 2 will tamper with the 2nd verified transaction in the blockchain 4 to check if the blockchain is valid or not enter check to run a validation test that will check the integrity of the blockchain and print a message accordingly 5 to show the usage instruction on the screen enter help to show the instructions 6 to terminate the program enter exit in the prompt or hit kbd ctrl kbd kbd c kbd usage add sender name receiver name transfer amount adds a block to blockchain with the given data show shows the list of blocks available in the blockchain tamper block number tampers with the block giver by the number check validates the blockchain help show this message exit close the program | blockchain blockchain-technology blockchain-demos educational | blockchain |
ntua-embedded | project hosted on gitlab due to lfs ntua embedded systems deisgn course https gitlab com xmpf ntua embedded | os |
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gcp-covid19-cloudfunctions | gcp covid19 cloudfunctions repository for covid19 data engineering using cloud functions in google cloud platform gcp datapipeline architecture images covid19 gcp datapipeline architecture jpg 1 replay covid19 filedates publish there is no trigger associated with this function this function is used for one time historical load or replay of file downloads for a range of dates the start and end dates are set as environment variables covid19 filedate events for the given dates are published at the target pub sub topic 2 daily covid19 filedate publish the trigger is http from the cloud scheduler it runs every night at 11 pm ist a covid19 filedate event is published at the target pub sub topic 3 daily covid19 filedownload the trigger is pub sub topic this function reads the covid19 filedate event from pub sub topic parses the file date from the message and then downloads the csv file from john hopkins cssegisanddata covid 19 github repository the downloaded file is then saved onto the cloud storage bucket 4 daily covid19 ingest bigquery the trigger is cloud storage bucket whenever a file gets updated or created in the covid19 file download cloud storage bucket this function will get triggered and the data will be loaded into the ingest raw data table in bigquery deployment from local machine set the global environment variables in the file env prod yaml set the cloud function specific configuration in scripts config yaml to deploy the cloud functions from the local machine navigate to scripts folder and run the following from command line python deploy py | cloud |
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SmartLock | smart lock embedded project on stm32 boards for the academic course of computer system design contributors gaetano marted biagio salzillo | os |
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dtt | dtt tabular transformer for joinability by leveraging large language models this repository contains resources developed within the following paper a dargahi nobari and d rafiei dtt an example driven tabular transformer by leveraging large language models you may check the paper https arxiv org abs 2303 06748 pdf https arxiv org pdf 2303 06748 for more information requirements several libraries are used in the project you can use the provided environment yml file to create the conda environment for the project if you prefer not to use the environment file the environment can be set up by the following command conda create n dtt python 3 10 conda activate dtt conda install pytorch 1 11 0 torchvision 0 12 0 torchaudio 0 11 0 torchtext 0 12 0 cudatoolkit 11 3 c pytorch conda install c huggingface transformers 4 21 1 conda install c conda forge pytorch lightning 1 7 3 conda install pandas 1 4 1 nltk 3 7 conda install pip pip install sentencepiece 0 1 97 pip install openai 0 26 5 to test with gpt3 usage three main directories are in the repo models data and src models this directory contains the trained model due to the size limit of github this folder is not added to the repository our trained model with default settings can be downloaded here https drive google com file d 1 7xtf9p7dzqpxbjykrsi sylk2wym34g view usp share link data all datasets are included in this directory please extract data tar gz to access the content of this folder the datasets are in datasets directory each dataset contains several tables each as a folder and each table contains source csv target csv and ground truth csv the datasets available in this file are ff aj the web tables and spreadsheet dataset tables starting with aj prefix belong to the web tables and those starting with ff belong to the spreadsheet dataset synthetic basic 10tr 100rows 08 35len the syn dataset reported in the table it contains 10 tables of synthetic tabular transformations single replace 05tr 050rows 08 35len the syn rp dataset reported in the table it contains 5 tables of simple synthetic tabular transformations single substr 05tr 050rows 08 35len the syn st dataset reported in the table it contains 5 tables of medium substring synthetic tabular transformations single replace 05tr 050rows 08 35len the syn rv dataset reported in the table it contains 5 tables of difficult reverse synthetic tabular transformations replace lens reverse lens substr lens are groups of datasets similar to the syn rp syn st and syn rv datasets but with input length varying from 5 to 60 before using each dataset to test the model it should be broken to training examples and test set src the source files are located in src directory this directory contains three sub directories data processor files in this folder are to generate pre process and use datasets table2sample py it takes a dataset as input and transforms it into the sample set that can be used for training the model table breaker see details for each file table breaker py this file will break a dataset into training examples and a test set run breaker sh this file contains examples of using table breaker py and its command line arguments as well as using it for many datasets automatically synthetic generator string transformations to generate synthetic datasets see details for each file transformation just libraries of transformations not to be used directly basic generator py this file is used to generate a synthetic sample set that is used to train the model single basic generator py this file generates synthetic datasets with one transformation such as syn rp syn st and syn rv datasets that are used to test the model synthetic basic generator py this file generates synthetic datasets with several transformations such as syn dataset that is used to test the model deep models this directory includes files to train and use the model byt5 to train and run a basic test with the main model based on byt5 model see details for each file byt5trainer py this file will train finetune a model given a set of training data load model py a basic example of using the trained model for transformation if you are interested in running the model without any finetuning or changes just download the pretrained model provided in the models section set the model path in model path variable and run the file t5 the same setting for t5 model the t5 based model is not complete and is only used to run some tests joineval py libraries to evaluate the performance of table joining util py some common functions tester py this is a large file used to test the model on various datasets the parameters can be set inside the code or be passed via command line arguments there are three files that exemplify how the tester file may be used run tester sh this file is an example of how a single instance of tester py can be called also it contains shell code to test several models on various datasets run len tester sh this file is an example of running tester py on datasets with various input lengths run gpt tester sh this file uses tester py with gpt 3 model please make sure your openai api key is stored in openai key file in deep models directory analyzer the codes in this directory just summarize the results of the model into short tables result tlb summary py get the summary of output when several models are used to transform the data len tlb summary py get the summary of output when several input lengths are experimented citation please cite the paper if you used the codes in this repository | deep-learning nlp | ai |
warehouse-system-management | warehouse system management repository project akhir matakuliah kapita software engineering aplikasi gudang berbasis web aplikasi dirancang menggunakan bahasa pemrograman python dan menggunakan framework flask untuk front end dan untuk back end menggunakan framework falcon aplikasi ini kemudian akan dideploy di google cloud database menggunakan postgresql | cloud |
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ESP8266-RTOS-HomeKit | esp8266 rtos homekit accessory work in progress native apple s homekit accessory implementation for the esp8266 based on freertos esp open rtos https github com superhouse esp open rtos updates about the development are available at development md https github com luigifreitas esp8266 rtos homekit blob master development md file required functions x mdns txt discovery x tlv decode encode x pairing step m1 m2 pairing step m3 m4 pairing step m5 m6 pair verify m1 m2 m3 m4 add pairing remove pairing list pairing example output output pairing step m1 m2 tcp new client connected tcp request received debug header have 119 bytes debug payload have 6 bytes 0x00 0x01 0x00 0x06 0x01 0x01 tlv tag received pairing method tlv tag received pairing process m1 tcp writing payload with 409 bytes tcp response sent tcp client disconnected thanks 1 nordic nrf51 homekit library https github com aanon4 homekit with some modifications this library worked very well for the esp8266 in this project it handles the crypto stuff tlv encoding decoding and srp protocol required by homekit big thanks to aanon4 https github com aanon4 2 tweetnacl http tweetnacl cr yp to crypto sha512 curve | esp8266 homekit freertos | os |
WebAR-Article | webar article about article is a responsive and information rich website that is progressively enhanced with augmented reality ar content exposed through experimental web technologies check out article in action here https google ar github io webar article public https google ar github io webar article public to learn more about article check out the blog post here https www blog google products google vr augmented reality web everyone https www blog google products google vr augmented reality web everyone to learn more about the technologies behind webar article please visit https developers google com ar develop web getting started https developers google com ar develop web getting started getting started prerequisites webar article will work everywhere desktop tablet and mobile however if you want to experience webar article s ar content make sure you have installed webaronarkit https github com google ar webaronarkit if you are on ios or webaronarcore https github com google ar webaronarcore on android if you aren t familar with these experimental browsers please visit their respective github repository to learn how to get starting webar article is built using modern web development tooling so before getting started make sure you have node https nodejs org en and the npm package manager https www npmjs com get npm installed on your development machine installing clone this repository and change directories into it and then install its dependencies git clone git github com google ar webar article git article cd article npm install running to view the site on desktop start the webpack dev server by the following command npm run dev then open your browser and navigate to localhost 8000 to view article s ar content open either webaronarcore on android or webaronarkit on ios and navigate the browser to the ip address of the machine serving the site followed by the port 8000 i e http xxx xxx xxx xxx 8000 building to build the site for deployment run the following command npm run build once built all the build files will be in the public folder contributing if you have fixes we would love to have your contributions please read contributing md for more information on the process we would like contributors to follow license apache license version 2 0 see the license file inside this repo disclaimer this is not an official google product built with three js https threejs org three ar js https github com google ar three ar js draco https github com google draco poly https poly google com bootstrap http getbootstrap com bootswatch https bootswatch com credits wikipedia https en wikipedia org wiki space suit poly model https poly google com view dlhpzndygsg links webaronarcore https github com google ar webaronarcore webaronarkit https github com google ar webaronarkit | webar augmented-reality webgl webvr webaronarkit webaronarcore webxr ar vr | server |
ITS-Support | its support information technology support | server |
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letscode-php | letscode php this is my attempt to learn backend development using the https app letscode hu roadmaps php backend course | server |
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sarafu | deployment to heroku git init git add a git commit m initial commit heroku create git push heroku master heroku run python manage py migrate further reading gunicorn https warehouse python org project gunicorn whitenoise https warehouse python org project whitenoise dj database url https warehouse python org project dj database url | front_end |
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Automated_data_pipeline | automated data pipeline data engineering automated data pipeline on mysql cloud database the objective of this project is to create a data pipeline that gathers information from the internet processes it and stores it in a database transitioning from a local setup to a cloud based infrastructure the project is divided into two main phases local pipeline and cloud pipeline phase 1 local pipeline in this phase the focus is on setting up a local environment for data collection and storage 1 1 scrape data from the web learn how to access and extract information from websites by downloading and parsing their html code using the beautiful soup library in python 1 2 collect data with apis acquire data from various internet data providers using apis learn how to authenticate assemble requests and interact with apis using python s requests library 1 3 create a database model define the logical structure of a relational database to store the collected data determine the required tables and their relationships paving the way for efficient data storage and retrieval 1 4 store data on a local mysql instance set up a local mysql database on your computer and store the collected data from both web scraping and apis ensure that the connection between python and mysql is functional phase 2 cloud pipeline in this phase the project transitions to a cloud based infrastructure for improved scalability and automation 2 1 set up a cloud database utilize amazon web services aws relational database service rds to create a cloud hosted mysql database this step improves the scalability and accessibility of the database 2 2 move scripts to lambda migrate the data collection scripts from jupyter notebooks to aws lambda functions lambda is a cloud service that allows code execution without managing server infrastructure 2 3 automate the pipeline leverage aws cloudwatch events or eventbridge to schedule and automate the execution of data collection scripts this automation simplifies the process and ensures data collection occurs at specific intervals or based on triggers overall this project aims to create an efficient and automated data pipeline initially on a local environment and later transitioning to a cloud based setup utilizing aws services to enhance scalability and maintainability architecture collect data with web scraping collect data with apis image https github com fabiano2415 automated data pipeline assets 101226686 c7ba0363 9314 41cc 9302 90c649bc69b6 | server |
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Flutter-Raptor | flutter raptor the app is design in flutter to be able to read contacts send sms and make a call through app it self app has finger print authentication this app is to design features which a mobile has app is able to connect to a backend server to save profile details this app is currently in the process of development the idea of raptor is to have a capibility to detect incoming caller id like true caller currently the app is available only for android list of fatures 1 finger print authentication 2 create profile 3 save profile in backend server through api 4 read add and delete contacts 5 read messages send message from app 6 read call history 7 responsive ui 8 change theme dark and light mode 9 extract device id iemi uuid to provide security against different frauds 10 use camera to save images todo list able to make phone calls from raptor itself detect caller id like truecaller apps flutter for ios | server |
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g_algorithm | g algorithm npm https img shields io npm v g algorithm svg https www npmjs com package g algorithm build status https api travis ci org cllgeek g algorithm svg branch master coverage status https coveralls io repos github cllgeek g algorithm badge svg branch master https coveralls io github cllgeek g algorithm branch master license mit https img shields io badge license mit yellow svg https www npmjs com package g algorithm javascript js star https github com cllgeek g arithemetic issue pr leetcode https leetcode com virtual judge https vjudge net careercup https www careercup com hackerrank https www hackerrank com codefights https codefights com gainlo http www gainlo co code guide http alloyteam github io codeguide js https www geekjc com post 5a5f2ef845e00518fed170b9 continuous integration ci ci javascript ci https www geekjc com book 5a9f552acb134c0648b75978 mocha http www ruanyifeng com blog 2015 12 a mocha tutorial of examples html karma http taobaofed org blog 2016 01 08 karma origin 4 4 1 1 2 3 function binary search arr key var low 0 high arr length 1 while low high var mid parseint high low 2 if key arr mid return mid else if key arr mid low mid 1 else if key arr mid high mid 1 else return 1 var arr 1 2 3 4 5 6 7 8 9 10 11 23 44 86 var result binary search arr 10 alert result 9 function binary search arr low high key if low high return 1 var mid parseint high low 2 if arr mid key return mid else if arr mid key high mid 1 return binary search arr low high key else if arr mid key low mid 1 return binary search arr low high key var arr 1 2 3 4 5 6 7 8 9 10 11 23 44 86 var result binary search arr 0 13 10 alert result 9 4 2 4 2 1 1 2 3 js function bubble sort arr for var i 0 i arr length 1 i for var j 0 j arr length i 1 j if arr j arr j 1 var swap arr j arr j arr j 1 arr j 1 swap var arr 3 1 5 7 2 4 9 6 10 8 bubble sort arr console log arr 4 2 2 js function quick sort arr if arr length 1 return arr var pivotindex math floor arr length 2 var pivot arr splice pivotindex 1 0 var left var right for var i 0 i arr length i if arr i pivot left push arr i else right push arr i return quick sort left concat pivot quick sort right var arr 5 6 2 1 3 8 7 1 2 3 4 7 console log quick sort arr 4 2 3 1 2 3 4 3 5 6 2 js function insert sort arr var i 1 j key len arr length for i len i var j i var key arr j while j 1 if arr j key arr j 1 arr j else break arr j 1 key return arr insert sort 2 34 54 2 5 1 7 5 issue pr | front_end |
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project-data-warehouse | data engineer nanodegree subproject the repository contains the project information of the data warehouse with aws redshift from udacity nanodegree data engineer https www udacity com course data engineer nanodegree nd027 please refer to the course website https www udacity com course data engineer nanodegree nd027 for more details br project scenario a startup called sparkify has grown their user base and song database and want to move their data process onto the cloud their data resides in aws s3 including user activity logs and song metadata in json format br business process data requirements analytics team wants to understand what songs their users are listening to by analyzing a set of dimensional tables analytics team wants a data warehouse on the cloud with tables designed to optimize queries and gain insights on song plays engineering task create and launch a redshift cluster on aws create a redshift cluster and iam role to grant access to s3 create a star schema and etl pipeline to prepare the data for analytics team explore load raw data json in s3 to redshift staging tables define fact dimension tables for a star schema for this particular analytic purpose write an etl pipeline to load data from staging tables to analytics tables on redshift connect to the redshift cluster and run some test queries tools used python 3 aws https aws amazon com redshift sql https docs aws amazon com redshift latest dg welcome html python configparser https docs python org 3 library configparser html psycopg2 https pypi org project psycopg2 lucidchart https www lucidchart com original data sources note that the actual data in json used in this project is a subset of original dataset preprocessed by the course the provided data resides in aws s3 publically available 1 song data from million song dataset http millionsongdataset com 2 user activity data from event simulator https github com interana eventsim based on million song dataset http millionsongdataset com database schema data warehousing design user story a user plays a song whose artist is artist name at time start time using agent br from the above story we can extract the necessary information dimensions who users dimension what songs and artists dimension when time dimension how many songplays fact more possible dimensions but not used in this project where locations dimension how agents dimension since the core business process metric is an user playing a song the fact table should store the song play records with user song identifier together with related information about the how and where the song is played based on the data and tables given in the project the star schema looks like this generated using lucidchart https www lucidchart com erd assets images erd png etl process usage and sample results setup configuration 1 setup your iam user with programmatic access key and secret key with the following commands export aws access key id your access key id export aws secret access key your secret access key br br note you can also setup your access key and secret key by storing them in config or credential files however we strongly recommend the above approach because you won t need to change the codes br 2 create your redshift cluster using the following scripts cluster start shutdown cluster cfg defines redshift cluster hardware database setup and region role names you can change them however you want cluster start shutdown start cluster py all in one script to launch the redshift cluster and handles iam role policy creation and securitygroup configurations we strongly recommend you to use this script to create the cluster and auto configure all related resources services you must have a running cluster in order to proceed with etl and query experiments later cluster start shutdown shutdown cluster py all in one script to shutdown the cluster and clean up all related resources for this project we strongly recommend you to run this script after you are done with the experiments project to prevent any further costs induced by aws redshift services 3 fill the host and arn in dwh cfg you can get their values by runing cluster start shutdown start cluster py in previous step 4 run create tables py 5 run etl py to load data from s3 into staging tables and then transfer into target tables fact and dimension tables you may choose a smaller dataset s3 udacity dend song data 4 min runtime instead of a complete one s3 udacity dend song data 2 hours runtime for demonstration purpose br note that you can use noload option in copy command to verify json data correctness integrity without loading the actual data into tables you can then check errors or violation of data format by running the query select from pg catalog stl load errors on redshift cluster sample queries note that you must finish the etl process before running the sample queries 1 top 10 most played songs query select sp song id s title count as cnt from songplays sp join songs s on sp song id s song id group by 1 2 order by 3 desc limit 10 result query1 assets images query1 png br 2 top 10 most played artists query select sp artist id a name as artist name count as cnt from songplays sp join artists a on sp artist id a artist id group by 1 2 order by 3 desc limit 10 result query2 assets images query2 png br 3 statistics on when songs are played during a day query select case when t hour between 2 and 8 then 2 8 when t hour between 9 and 12 then 9 12 when t hour between 13 and 18 then 13 18 when t hour between 19 and 22 then 19 22 else 23 24 0 2 end as play time count as cnt from songplays sp join time t on sp start time t start time group by 1 order by 2 desc result query3 assets images query3 png we can tell from the above result that most users play songs in the afternoon between 13 00 and 18 00 and very few users play songs in late mid night 4 another interesting query would be aggregating song play history for each user and use it for machine learning recommendation implementation details notes 1 redshift does not enforce not null constraint on primary key foreign key and reference table name column name 2 redshift does not enforce uniqueness constraint on primary key however the auto generated identity column is guaranteed to have unique value across entire table 3 redshift does not enforce referential integrity on keyword reference table name column name or foreign key they are only used by query planers to optimize performance todos 1 analyze table design and performance via trying different distribution styles and sorting keys resources 1 intro to aws redshift cluster management https docs aws amazon com redshift latest mgmt welcome html how to create and manage redshift clusters on aws 2 developer guide to aws redshift https docs aws amazon com redshift latest dg welcome html how to create and develop data warehouses using redshift 3 python sql client driver psycopg2 https www psycopg org docs how to use psycopg2 to connect to sql postgresql compatible databases and execute queries 4 psycopg2 with redshift https rudderstack com blog access and query your amazon redshift data using python and r how to use psycopg2 to connect to databases on aws redshift and execute queries 5 redshift python sdk boto3 https boto3 amazonaws com v1 documentation api latest index html | cloud |
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frameworkless-front-end-development | apress source code for frameworkless front end development framework less http frameworklessmovement org img frameworkless badge github svg https github com frameworkless movement manifesto javascript style guide https img shields io badge code style standard brightgreen svg https standardjs com this repository accompanies frameworkless front end development http www apress com 9781484249666 by francesco strazzullo apress 2019 comment cover cover image 9781484249666 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository chapters chapter 1 let s talk about frameworks https github com apress frameworkless front end development tree master chapter01 chapter 2 rendering https github com apress frameworkless front end development tree master chapter02 chapter 3 managing dom events https github com apress frameworkless front end development tree master chapter03 chapter 4 web components https github com apress frameworkless front end development tree master chapter04 chapter 5 http requests https github com apress frameworkless front end development tree master chapter05 chapter 6 routing https github com apress frameworkless front end development tree master chapter06 chapter 7 state management https github com apress frameworkless front end development tree master chapter07 | front_end |
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Validator | license lgpl v3 https img shields io badge license lgpl 20v3 blue svg https www gnu org licenses lgpl 3 0 korean https img shields io badge language korean blue svg korean a name korean a opengds validator opengds validator sw 3 br 2 geotools geoservermanager opengds builder br br link http www git co kr br opengeodt opengds validator gis geodt 2 2 ogc 53 shp ngi dxf shp link http www pusan ac kr link http www krihs re kr getting started 1 java openjdk 1 8 0 111 64 bit eclipse neon tomcat8 0 43 64bit 2 clone or download https github com odtbuilder validator git svn clone zip 3 eclipse project import 4 test src test com git gdsbuilder validator validationtest java dtlayercollection validation zip shp layer pre code 1 read zip file file zipfile new file d digitalmap20 zip unzipfile unzipfile new unzipfile d upzip try unzipfile decompress zipfile long 2 cidx 2 shp catch throwable e e printstacktrace 2 create dtlayercollection string epsg epsg 4326 qafileparser parser new qafileparser epsg 2 shp unzipfile null cidx 2 shp dtlayercollectionlist collectionlist parser getcollectionlist 3 create qalayertype string typename 4 set selfentity option graphicmiss selfentity new graphicmiss selfentity setoption dmqaoptions selfentity geterrcode 5 set entityduplicated option graphicmiss entityduplicated new graphicmiss entityduplicated setoption dmqaoptions entityduplicated geterrcode list graphicmiss graphicmissoptions new arraylist graphicmissoptions add selfentity graphicmissoptions add entityduplicated 6 create qaoption qaoption option new qaoption option setname typename option setgraphicmissoptions graphicmissoptions qalayertype layertype new qalayertype layertype setname typename list string layeridlist new arraylist layeridlist add b0010000 layeridlist add b0020000 layertype setlayeridlist layeridlist layertype setoption option qalayertypelist qalayertypelist new qalayertypelist qalayertypelist add layertype 7 validation for dtlayercollection collection collectionlist collectionvalidator validator new collectionvalidator collection null qalayertypelist errorlayer errlayer validator geterrlayer try 8 write error shp file shpfilewriter writeshp epsg errlayer d collectionerr collection getcollectionname shp catch ioexception schemaexception factoryexception e e printstacktrace code pre dtlayer validation shp layer pre code 1 read shp file string epsg epsg 4326 shpfilelayerparser parser new shpfilelayerparser simplefeaturecollection sfc parser getshpobject epsg new file d gis osm buildings shp 2 create dtlayer string layerid gis osm buildings dtlayer layer new dtlayer layer setlayerid layerid layer setsimplefeaturecollection sfc 3 validation layervalidator validator new layervalidatorimpl layer try errorlayer errlayer validator validateselfentity null 4 write error shp file shpfilewriter writeshp epsg errlayer d layererr shp catch schemaexception ioexception factoryexception e e printstacktrace code pre dtfeature validation feature simplefeature pre code 1 create geometry geometryfactory geometryfactory jtsfactoryfinder getgeometryfactory null wktreader reader new wktreader geometryfactory try geometry geom1 geometryfactory creategeometry reader read polygon 10 10 30 0 40 10 30 20 10 10 geometry geom2 geometryfactory creategeometry reader read polygon 20 10 20 40 30 40 30 0 20 10 2 create simplefeature simplefeaturetype sftype1 datautilities createtype dtfeature1 the geom polygon simplefeature sf1 simplefeaturebuilder build sftype1 new object geom1 dtfeature1 simplefeaturetype sftype2 datautilities createtype dtfeature2 the geom polygon simplefeature sf2 simplefeaturebuilder build sftype2 new object geom2 dtfeature2 3 create dtfeature dtfeature feature1 new dtfeature feature1 setsimefeature sf1 dtfeature feature2 new dtfeature feature2 setsimefeature sf2 4 validation featuregraphicvalidator validator new featuregraphicvalidatorimpl list errorfeature errfeatures validator validateselfentity feature1 feature2 null errorlayer errlayer new errorlayer errlayer adderrorfeaturelist errfeatures 5 write error shp file string epsg epsg 4326 shpfilewriter writeshp epsg errlayer d featureerr shp catch parseexception schemaexception e e printstacktrace catch nosuchauthoritycodeexception e e printstacktrace catch ioexception e e printstacktrace catch factoryexception e e printstacktrace code pre error shp file error shp file opengds builder gis api javadoc 1 geotools 16 5 lgpl http www geotools org 2 jts 1 12 eclipse public license 1 0 eclipse distribution license 1 0 a bsd style license https www locationtech org 2 apachepoi 3 14 apache license 2 0 http poi apache org 3 apachecommons 1 3 3 apache license 2 0 commons apache org proper commons logging 4 jackson 1 9 7 apache license al 2 0 lgpl 2 1 5 json 20160212 mit license 6 kabeja 0 4 apache software license 2 0 http kabeja sourceforge net index html 7 kabeja svg 0 4 apache software license 2 0 http kabeja sourceforge net index html 8 gt datastore ngi 1 0 0 gnu library or lesser general public license version 2 0 lgplv2 http www mangosystem com mail developer sg lee ghre55 git co kr | server |
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mws-restaurant-stage-3 | local development api server usage get restaurants curl http localhost 1337 restaurants get restaurants by id curl http localhost 1337 restaurants 3 architecture local server node js sails js contributors brandy lee camacho technical project manager mailto brandy camacho udacity com david harris web services lead mailto david harris udacity com omar albeik frontend engineer mailto omaralbeik gmail com getting started development local api server location of server server server depends on node js lts version v6 11 2 https nodejs org en download npm https www npmjs com get npm and sails js http sailsjs com please make sure you have these installed before proceeding forward great you are ready to proceed forward awesome let s start with running commands in your terminal known as command line interface cli install project dependancies install project dependancies npm i install sails js globally install sails global npm i sails g start the server start server node server you should now have access to your api server environment debug environment development debug port 1337 endpoints get endpoints get all restaurants http localhost 1337 restaurants get favorite restaurants http localhost 1337 restaurants is favorite true get a restaurant by id http localhost 1337 restaurants restaurant id get all restaurant reviews http localhost 1337 reviews get a restaurant review by id http localhost 1337 reviews review id get all reviews for a restaurant http localhost 1337 reviews restaurant id restaurant id post endpoints create a new restaurant review http localhost 1337 reviews parameters restaurant id restaurant id name reviewer name rating rating comments comment text put endpoints favorite a restaurant http localhost 1337 restaurants restaurant id is favorite true unfavorite a restaurant http localhost 1337 restaurants restaurant id is favorite false update a restaurant review http localhost 1337 reviews review id parameters name reviewer name rating rating comments comment text delete endpoints delete a restaurant review http localhost 1337 reviews review id if you find a bug in the source code or a mistake in the documentation you can help us by submitting an issue to our waffle dashboard https waffle io udacity mwnd issues even better you can submit a pull request with a fix archival note this repository is deprecated therefore we are going to archive it however learners will be able to fork it to their personal github account but cannot submit prs to this repository if you have any issues or suggestions to make feel free to utilize the https knowledge udacity com forum to seek help on content specific issues submit a support ticket along with the link to your forked repository if learners are blocked for other reasons here are the links for the retail consumers https udacity zendesk com hc en us requests new and enterprise learners https udacityenterprise zendesk com hc en us requests new ticket form id 360000279131 | front_end |
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next-mobile-boilerplate | next mobile boilerplate a next mobile boilerplate using ant design mobile with full support from development to deployment features ant design mobile as mobile ui express server customization combines prettier and eslint in pre commit hook using lint staged stop worrying about shit code slip into your code base pm2 as the production process manager http proxy middleware for remote server api proxy to avoid cors error assets hash for production static resources version control getting started bash git clone https github com posrix next mobile boilerplate my project cd my project npm install npm run dev deployment first git clone your project to production server then simply type sh deploy sh by default production server will listen at port 3100 modify config env js for customization license the mit license mit please see license file license md for more information | nextjs react ssr mobile boilerplate ant-design-mobile | front_end |
soft-ui-react-native | soft ui react native https demos creative tim com soft ui react native index html tweet https img shields io twitter url http shields io svg style social logo twitter https twitter com home status soft ui 20react 20native 20a 20cool 20nowui 20react 20native 20app 20template 20 e2 9d a4 ef b8 8f 20https 3a bit ly 2kaj86h 20 23reactnative 20 23nowui 20 23designsystem 20 23developers 20via 20 40creativetim version https img shields io badge version 1 1 0 blue svg github issues open https img shields io github issues creativetimofficial ct soft ui react native svg style flat https github com creativetimofficial ct soft ui react native issues q is 3aopen is 3aissue github issues closed https img shields io github issues closed raw creativetimofficial ct soft ui react native svg maxage 2592000 https github com creativetimofficial ct soft ui react native issues q is 3aissue is 3aclosed product https s3 amazonaws com creativetim bucket products 490 original opt soft ui react native thumbnail jpg soft ui react native is a fully coded app template built over react native https facebook github io react native ref creativetim and expo https expo io ref creativetim to allow you to create powerful and beautiful e commerce mobile applications we have redesigned all the usual components in house to make it look like soft ui s kit minimalistic and easy to use start your development with a design system for react native inspired by soft ui kit if you like soft ui s kit you will love this react native app template it features a huge number of components and screens built to fit together and look amazing fully coded components soft ui react native features over 100 variations of components like buttons inputs cards navigations etc giving you the freedom of choosing and combining all components can take variations in colour that you can easily modify inside our theme file you will save a lot of time going from prototyping to full functional code because all elements are implemented we wanted the design process to be seamless so switching from image to the real page is very easy to do components cards soft ui react native comes packed with a large number of components and cards putting together a mobile app has never been easier than matching together different components from the profile screen to a settings screen you can easily customise and build your screens we have created multiple options for you to put together and customise into pixel perfect screens view all components here https demos creative tim com soft ui react native example screens if you want to get inspiration or just show something directly to your clients you can jump start your development with our pre built example screens from onboarding screens to profile or discover screens you will be able to quickly set up the basic structure for your react native mobile project view all screens here https demos creative tim com soft ui react native screens let us know your thoughts below and good luck with development table of contents versions versions demo demo quick start quick start documentation documentation file structure file structure os support os support resources resources reporting issues reporting issues technical support or questions technical support or questions licensing licensing useful links useful links versions img src https github com creativetimofficial public assets blob master logos html logo jpg raw true width 60 height 60 https www creative tim com product soft ui kit pro img src https github com creativetimofficial public assets blob master logos react native logo jpg raw true width 60 height 60 https www creative tim com product soft ui react native html react native soft ui kit https s3 amazonaws com creativetim bucket products 448 original opt sds free thumbnail jpg 1614876201 https www creative tim com product soft ui design system soft ui react native https s3 amazonaws com creativetim bucket products 490 original opt soft ui react native thumbnail jpg 1625576346 https www creative tim com product soft ui react native demo start page https demos creative tim com soft ui react native quick start https www creative tim com learning lab react native quick start soft view more https demos creative tim com soft ui react native quick start try it out on expo simulator for ios or even your physical device if you have an android download from creative tim https www creative tim com product soft ui react native documentation the documentation for the soft ui react native is hosted at our website https www creative tim com learning lab react native overview soft file structure within the download you ll find the following directories and files soft ui react native app tsx readme md app json assets babel config js package json src assets fonts icons images components article tsx block tsx button tsx checkbox tsx image tsx input tsx modal tsx product tsx switch tsx text tsx index tsx constants index ts light ts mocks ts regex ts theme ts translations en json index ts types components ts index ts theme ts hooks index ts usedata tsx usescreenoptions tsx usetheme tsx usetranslation tsx navigation app tsx menu tsx screens tsx screens articles tsx components tsx home tsx pro tsx profile tsx register tsx index ts tsconfig json os support at present we officially aim to support the last two versions of the following operating systems img src https raw githubusercontent com creativetimofficial ct material kit pro react native master assets android logo png width 60 height 60 https www creative tim com product soft ui react native img src https raw githubusercontent com creativetimofficial ct material kit pro react native master assets apple logo png width 60 height 60 https www creative tim com product soft ui react native resources demo https demos creative tim com soft ui react native download page https www creative tim com product soft ui react native documentation https www creative tim com learning lab react native overview soft license agreement https www creative tim com license support https www creative tim com contact us issues github issues page https github com creativetimofficial ct soft ui react native issues soft ui design system https www creative tim com product soft ui design system ref soft uiprn readme for front end development reporting issues we use github issues as the official bug tracker for the soft ui react native here are some advices for our users that want to report an issue 1 make sure that you are using the latest version of the soft ui react native 2 providing us reproducible steps for the issue will shorten the time it takes for it to be fixed 3 some issues may be browser specific so specifying in what browser you encountered the issue might help technical support or questions if you have questions or need help integrating the product please contact us https www creative tim com contact us instead of opening an issue licensing copyright 2021 creative tim https www creative tim com creative tim license https www creative tim com license useful links tutorials https www youtube com channel ucvytg4scw rovb9ohkzzd1w affiliate program https www creative tim com affiliates new earn money blog creative tim http blog creative tim com free products https www creative tim com bootstrap themes free from creative tim premium products https www creative tim com bootstrap themes premium from creative tim react products https www creative tim com bootstrap themes react themes from creative tim angular products https www creative tim com bootstrap themes angular themes from creative tim vuejs products https www creative tim com bootstrap themes vuejs themes from creative tim more products https www creative tim com bootstrap themes from creative tim check our bundles here https www creative tim com bundles ref soft ui github readme social media twitter https twitter com creativetim facebook https www facebook com creativetim dribbble https dribbble com creativetim instagram https www instagram com creativetimofficial | react-native creative-tim expo soft-ui | os |
ITPM_Fall_2021 | itpm fall 2021 information technology process management | server |
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OSProject | auburn university elec 5260 6260 6266 final project real time operating systems rtos project requirments design and implement a project that will exercise the arm cmsis rtos real time operating system on the stm32l476g discovery board the project must contain the following elements 1 the project must comprise at least seven different rtos threads 2 the project must periodically acquire and use data in some way from each of the following three sensors on the stm32l476g discovery board 1 l3gd20 3 axis gyroscope 2 lsm30ctr 3d magnetometer 3 lsm30ctr 3d accelerometer magnetometer and accelerometer are on the same ic 3 project related information must be displayed and updated on the lcd 4 optionally you may use leds and or joystick in some way 5 the project must exercise the following rtos features in some appropriate way 1 one or both of thread flags and event flags 2 one or both of mutex and semaphore 3 message queue you may design the project to do whatever you wish provided that it meets the above requirements the final project should be demonstrated in my office by the deadline the following deliverables should be provided at that time 1 a one or two page description of the project 2 a hard copy of your programs not the drivers 3 a zip file of your project the entire project directory so that i could run it later cmsis rtos rtx information and resources in the books pane of uvision5 under tools user s guide the document mdkarm getting started pdf contains a chapter on cmsis rtos2 pages 26 36 on page 29 of that document is a link to the keil web site that contains complete information on cmsis rtos2 including tutorials for creating projects configuring cmsis rtos rtx and using the different rtos functions and data structures http www keil com pack doc cmsis rtos2 html index html the keil installation directory has a sample blinky project that uses cmsis rtos2 on a different board stm32l496g discovery from ours but is similar on my pc this is located at c keil arm pack keil stm32l4xx dfp 2 0 0 mdk boards st stm32l476g disco blinky you can copy the blinky project to your own directory and modify its settings for your board open the pack installer button in the menu bar select stm32l496g discovery in the boards tab and then select cmsis rtos2 blinky in the examples tab project description setup main c this includes the proper headers for the gyroscope and compass l3gd20 and lsm303c the main function sets up all the bsp helpers that we need to make use of the hardware it sets up the system clock to use a faster clock frequency to allow for more precise thread management and real time clock it enables the hal delay function to use the oskernelticks instead of the hal ticks by modifying the hal gettick void function finally it enables the joystick to communicate with the threads sel button toggles the workflag to enable disable leds and clock up down buttons switches which sensor data is being displayed left right buttons switches which dimension data is being displayed the joystick allows these threads to be managed asynchronously threads 1 app main initializes the state variables and creates other threads this does not run after its initial run 2 gyro gets data from the gyroscope every 50ms it first acquires the datamutex to make sure that something isn t trying to access the data at the same time this also ensures that other threads won t be getting data at the same time 3 acc gets data from the accelerometer every 50ms it first acquires the datamutex to make sure that something isn t trying to access the data at the same time this also ensures that other threads won t be getting data at the same time 4 gyro gets data from the magnetometer every 50ms it first acquires the datamutex to make sure that something isn t trying to access the data at the same time this also ensures that other threads won t be getting data at the same time 5 compute first acquires the datamutex it then calculates if a specific data value is positive or negative this data value is specified by the state variables sensor and dimension it then turns on the red led if the data is negative or turns on the green led if the data is positive it turns off whatever led is not in use if the value is 0 it turns off both the green and red leds before it manages the led states it releases the datamutex because it is no longer accessing the data this runs every 50ms when the workflag is enabled if the workflag is disabled it turns off both the green and red leds 6 printcharacters prints the state variables depending on what sensor is selected it will print gyr acc or mag gyroscope accelerometer magnetometer respectively it will also print an x y or z after the three letter to display the dimension state it will only print the values when they are changed and does this based on the thread flag it waits until one of the first 3 lsb least significant bit s are raised then after acquiring the displaymutex prints the sensor characters if the 3rd lsb is high and prints the dimension character if the 2nd lsb is high it clears the bits that are high when first sees the flag to prevent it from constantly updating the display when it doesn t need to the event flag is raised by the joystick interrupt which was mentioned earlier 7 printtime this thread prints a message which consists of 2 characters into the last two spots on the lcd it is called printtime because the clock is passing the message to it but it has the potential to print any data passed to it not just time this thread first prints 0 0 as an initial placeholder and then waits to receive the message before printing anything else the messagequeue it uses only allows for one message in the queue to prevent it from missing any data additionally before it prints any data except the initial data it acquires the displaymutex it releases the displaymutex after displaying the message 8 clock this is a real time clock which keeps track of seconds with a max of 99 seconds it is only enabled when the workflag is raised to ensure that it is an accurate clock it first calculates the tickcount that is one second away and then increments the time and passes the time using the clockprintqueue it also has a timeout of 500ms when attempting to write to the clockprintqueue to prevent the clock from delaying notes the printcharacters thread and printtime thread could be easily combined to use one method or the other and are only separated to show different ways to do it | auburn elec5260 embedded cmsis cmsis-os2 vscode elec6260 | os |
crux | crux a href https clh7rniov2 execute api us east 1 amazonaws com express target blank rel noopener img title quorum slack src https clh7rniov2 execute api us east 1 amazonaws com express badge svg alt quorum slack a a href https travis ci org blk io crux img title build status src https travis ci org blk io crux svg branch master alt build status a a href https goreportcard com report github com blk io crux img title go report card src https goreportcard com badge github com blk io crux alt go report card a data privacy for quorum crux is a secure enclave for quorum written in golang it is a replacement for constellation https github com jpmorganchase constellation the secure enclave component of quorum https github com jpmorganchase quorum written in haskell getting started 4 node quorum network with crux the best way to start is to run the quorum crux docker image https github com blk io crux tree master docker quorum crux this image runs a 4 node quorum network using crux as the secure enclave communicating over grpc bash git clone https github com blk io crux git docker compose f docker quorum crux docker compose yaml up where the node details are as follows name quorum node address account key crux node key quorum1 http localhost 22001 0xed9d02e382b34818e88b88a309c7fe71e65f419d buler8jyuwhiuucmu hla0q5pzkyt chii3zkbey3bo quorum2 http localhost 22002 0xca843569e3427144cead5e4d5999a3d0ccf92b8e qfedays9mpds2xhextc84jkghxzg aj52dth0vta3xc quorum3 http localhost 22003 0x0fbdc686b912d7722dc86510934589e0aaf3b55a 1itzde ndbhvzhcl7v68x44vx7pl8nwx9lqnm afjug quorum4 http localhost 22004 0x9186eb3d20cbd1f5f992a950d808c4495153abd5 onspppgszvufw0qmgffwwh1uxvuxgvbxlexorhj07g8 local docker if you want to make changes to e g istanbul start sh then build the docker image locally docker compose f docker compose local yaml up build 2 node crux only network 2 crux nodes example https github com blk io crux tree master docker crux is simple docker image to just bring up 2 crux nodes which communicate with each other bash git clone https github com blk io crux git docker compose f docker crux docker compose yaml up where the crux node keys are the same as quorum1 and quorum2 above and are listening on ports 9001 and 9002 for grpc requests vagrant vm for those of you who are unable to use docker you can run the 7 nodes quorum example https github com blk io quorum examples which is an updated version of jp morgan s quorum 7 nodes example using crux as the secure enclave download the latest binary the latest binaries for different platforms are available on the release https github com blk io crux releases latest page generating keys each crux instance requires at least one key pair to be associated with it the key pair is used to ensure transaction privacy crux uses the nacl cryptography library https nacl cr yp to you use the generate keys argument to generate a new key pair with crux bash crux generate keys mykey this will produce two files named mykey key and mykey pub reflecting the private and public keys respectively core configuration at a minimum crux requires the following configuration parameters this tells the crux instance what port it is running on and what ip address it should advertise to other peers details of at least one key pair must be provided for the crux node to store requests on behalf of bash crux url http 127 0 0 1 9001 port 9001 workdir crux publickeys tm pub privatekeys tm key othernodes https 127 0 0 1 9001 build instructions if you d prefer to run just a client you can build using the below instructions and run as per the below bash git clone https github com blk io crux git cd crux make setup make bin crux usage of bin crux crux config optional config file alwayssendto string list of public keys for nodes to send all transactions too berkeleydb use berkeley db for working with an existing constellation data store experimental generate keys string generate a new keypair grpc use grpc server default true grpcport int the local port to listen on for json extensions of grpc default 1 networkinterface string the network interface to bind the server to default localhost othernodes string boot nodes to connect to to discover the network port int the local port to listen on default 1 privatekeys string private keys hosted by this node publickeys string public keys hosted by this node socket string ipc socket to create for access to the private api default crux ipc storage string database storage file name default crux db tls use tls to secure http communications tlsservercert string the server certificate to be used tlsserverkey string the server private key url string the url to advertise to other nodes reachable by them v v int verbosity level of logs shorthand default 1 verbosity int verbosity level of logs default 1 workdir string the folder to put stuff in default default how does it work at present crux performs its cryptographic operations in a manner identical to constellation you can read the specifics here https github com jpmorganchase constellation how it works the two main workflows for handling private transactions are the submission and retrieval demonstrated below new transaction submission new transaction sequence docs new tx svg existing transaction retrieval read transaction sequence docs read tx svg logical architecture logical architecture https github com blk io crux blob master docs quorum architecture png why crux crux is a constellation located in the southern sky in a bright portion of the milky way it is among the most easily distinguished constellations even though it is the smallest of all 88 modern constellations source wikipedia https en wikipedia org wiki crux the critical or transitional moment or issue a turning point thanks patrickmn https github com patrickmn the original author of constellation crux would not exist were it not for his work | golang quorum ethereum blockchain enterprise | blockchain |
long_tail_knowledge | large language models struggle to learn long tail knowledge this repo contains the code and data used for the analysis in large language models struggle to learn long tail knowledge https arxiv org abs 2211 08411 pre training dataset entities as part of this project we ran the dbpedia spotlight https www dbpedia spotlight org entity linker on large scale lm pre training datasets such as the pile c4 roots openwebtext and wikipedia the entity linking data is hosted on huggingface hub https huggingface co datasets nkandpa2 pretraining entities to get this data you can either download it from their web ui download it with python using the instructions from here https huggingface co docs huggingface hub how to downstream or run git clone https huggingface co datasets nkandpa2 pretraining entities to clone the huggingface repo note that this requires git lfs to be installed to actually download the data format there are five files the pile entity map npz c4 entity map npz roots entity map npz openwebtext entity map npz wikipedia entity map npz each file can be loaded with numpy load and is a dictionary mapping from dbpedia uri strings to numpy arrays of pre training dataset indices where that entity occurs qa dataset entities to analyze the effect of pre training entities on qa performance we entity link natural questions and trivia qa the qa entity linking data is hosted on huggingface hub https huggingface co datasets nkandpa2 qa entities to get this data you can either download it from their web ui download it with python using the instructions from here https huggingface co docs huggingface hub how to downstream or run git clone https huggingface co datasets nkandpa2 qa entities to clone the huggingface repo note that this requires git lfs to be installed to actually download the data format there are four files nq train entities jsonl nq validation entities jsonl trivia qa unfiltered nocontext train entities jsonl trivia qa unfiltered nocontext validation entities jsonl the natural questions files are in the order of the nq open https huggingface co datasets nq open dataset and the trivia qa files are in the order of the unfiltered nocontext split of the trivia qa https huggingface co datasets trivia qa dataset these are each jsonlines files each line in the file is a dictionary with the following structure q entities list of entities found in the question a entities list of entities found in the answer aliases the entities in the q entities and a entities lists are also dictionaries with the structure uri dbpedia entity uri support dbpedia support types dbpedia types surfaceform string in q or a that was linked to this entity offset location in q or a where surface form is found similarityscore dbpedia similarity score percentageofsecondrank dbpedia percentage of second ranked entity code the code and instructions for running the entity linker can be found in entity linking the code and instructions for running the relevant document counting heuristic can be found in relevant docs | ai |
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learn-computer-vision-using-opencv | apress source code this repository accompanies learn computer vision using opencv https www apress com 9781484242605 by sunila gollapudi apress 2019 comment cover cover image 9781484242605 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | ai |
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QGPTAgent | div style text align center img width 200px hight 200px src icon full png alt qgpt agent icon div qgpt agent documentation qgpt agent is a plugin for qgis that allows users to interact with qgis using natural language commands it utilizes the advanced natural language processing capabilities of the openai gpt model to automate various processes in qgis this significantly reduces the time and effort required to complete various tasks in qgis https user images githubusercontent com 20235263 235729556 5913541c c408 459d bfad 1ce754c14542 mp4 installation to install qgpt agent follow the steps below 1 open qgis and go to the plugins menu 2 select manage and install plugins 3 in the all tab search for qgpt agent 4 click on install plugin to install qgpt agent to install from zip file 1 download the plugin zip file from the plugin repository or from releases 2 open qgis and go to the plugins menu 3 select manage and install plugins 4 in the installed tab click on the install from zip button 5 in the install plugin dialog box click on the choose plugin zip button and select the downloaded zip file 6 click on install plugin to install the plugin 7 once the installation is complete you should see a message confirming the successful installation of the plugin 8 close and reopen qgis for the newly installed plugin to take effect open ai access tocken to get an openai api access token follow these steps 1 create an openai account at https beta openai com signup 2 once you ve created an account navigate to the api page at https platform openai com account api keys 3 click on the create new api key button to generate a new secret key 4 copy the api key and insert it in setting tab note be sure to keep your api key secure as it can be used to access your openai resources and make api calls on your behalf usage once qgpt agent is installed you can use natural language commands to interact with qgis to use qgpt agent follow the steps below 1 open qgis and load the data you want to work with 2 in the qgis menu go to plugins and select qgpt agent 3 the qgpt agent dialog box will appear type your natural language command in the text box provided 4 press enter or click on send to execute the command getting started a href https youtu be okyswwpduiw here a commands qgpt agent supports a wide range of natural language commands but try to write command properly chat it can be worked as chat to answer your questions about gis limitations qgpt agent is a powerful plugin that can automate various processes in qgis however it has some limitations that users should be aware of some of the limitations of qgpt agent are listed below qgpt agent requires an internet connection to use the openai gpt model for natural language processing qgpt agent may not recognize some natural language commands correctly qgpt agent may not be able to perform some complex tasks that require multiple steps or complex workflows qgpt agent may not be suitable for users who prefer a more hands on approach to working with qgis conclusion qgpt agent is a powerful plugin that can help users automate various processes in qgis using natural language commands it utilizes the advanced natural language processing capabilities of the openai gpt model to interpret natural language commands and execute them in qgis although qgpt agent has some limitations it can be a valuable tool for users who want to streamline their workflow and save time and effort when working with qgis to be done supporting step by step supervised execution suggestion by julius petri multiple prompt support adding new tab to edit code and run it save history and setting support complex analysis processes adding long term memory more prompt optimization disclaimer it is important to exercise caution when using the run directly mode in qgpt agent as running code without any supervision may lead to unintended consequences or errors while the natural language autonomous agent can automate tasks and save time it is still important to review the generated code before executing it this will ensure that the code aligns with your intended goals and does not cause any unintended side effects or errors contact email momaabna2019 gmail com | ai |
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sign_follower | sign follower by the end of the traffic sign follower project you will have a robot that will look like this video demo neato robot obeys traffic signs https youtu be porevhj1lsa system alt text system overview system overview images vision nav system overview png three stages of the vision and navigation system 1 waypoint navigation 2 sign recognition and 3 sign obeyance via changing the next waypoint navigation and mapping is handled by the built in ros package neato 2dnav mapping the location of the robot in the environment was handled by gmapping http wiki ros org gmapping a package that provides laser based slam simultaneous localization and mapping navigation was handled by the move base http wiki ros org move base package our program published waypoints to the move base simple goal topic while the internals of path planning and obstacle avoidance were abstracted away you will put your comprobo chops to the test by developing a sign detection node which publishes to a topic predicted sign once it is confident about recognizing the traffic sign in front of it running and developing the street sign recognizer node we ve provided some rosbags that will get you going uturn bag https drive google com open id 0b85lerk460tuyjfglvg1rxrwams rightturn bag https drive google com open id 0b85lerk460tun3zmuk15dmtptfk leftturn bag https drive google com open id 0b85lerk460tutkdtqw5yq0fwsee start by replaying these rosbags on loop rosbag play uturn bag l they have camera image raw compressed channel recorded in order to republish the compressed image as a raw image rosrun image transport republish compressed in camera image raw image transport compressed raw out camera image raw to run the node rosrun sign follower street sign recognizer py if you ran on the steps above correctly a video window should appear visualizing the neato moving towards a traffic sign detecting signs in scene images reliable detection of traffic signs and creating accurate bounding box crops is an important preprocessing step for further steps in the data pipeline you will implement code that will find the bounding box around the traffic sign in a scene we ve outlined a suggested data processing pipeline for this task 1 colorspace conversion to hue saturation value hsv image seen in the top left window 2 a filter is applied that selects only for objects in the yellow color spectrum the range of this spectrum can be found using hand tuning binarized image seen in the bottom window 3 a bounding box is drawn around the most dense yellow regions of the image yellow sign detector yellow sign detector images yellow sign detector gif bounding box generated around the yellow parts of the image the video is converted to hsv colorspace an inrange operation is performed to filter out any non yellow objects and finally a bounding box is generated you will be writing all your image processing pipeline within the process image callback function here is what the starter code looks like so far python def process image self msg process image messages from ros and stash them in an attribute called cv image for subsequent processing self cv image self bridge imgmsg to cv2 msg desired encoding bgr8 left top right bottom self sign bounding box left top left top right bottom right bottom crop bounding box region of interest cropped sign self cv image top bottom left right draw bounding box rectangle cv2 rectangle self cv image left top right bottom color 0 0 255 thickness 5 the goal of localizing the signs in the scene is to determine left top x1 y1 and right bottom x2 y2 points that define the upper lefthand corner and lower righthand corner of a bounding box around the sign you can do most of your work in the instance method sign bounding box python def sign bounding box self returns left top right bottom where left top and right bottom are tuples of x pixel y pixel defining topleft and bottomright corners of the bounding box todo your solution here left top 200 200 right bottom 400 400 return left top right bottom whether you follow along with the suggested steps for creating a sign recognizer or have ideas of your own revisit these questions often when designing your image processing pipeline what are some distinguishing visual features about the sign is there similarities in color and or geometry since we are interested in generating a bounding box to be used in cropping out the sign from the original frame what are different methods of generating candidate boxes what defines a good bounding box crop it depends a lot on how robust the sign recognizer you have designed finally if you think that working with individual images outside of the streetsignrecognizer class would be helpful i often like to prototype the computer vision algorithms i am developing in a jupyter notebook feel free to use some of the image frames in the images folder in addition you can save your own images from the video feed by using opencv s imwrite method http docs opencv org 2 4 modules highgui doc reading and writing images and video html highlight imwrite imwrite red green blue to hue saturation value images there are different ways to represent the information in an image a gray scale image has n rows n cols an rgb image has shape n rows n cols 3 since it has three channels red green and blue note as you saw in class and it is the case with the given starter code that opencv uses the channel ordering blue green red instead color images are also represented in different ways too aside from the default rgb colorspace there exists alot of others we ll be focused on using hsv hsl https en wikipedia org wiki hsl and hsv hue saturation and value luminosity like rgb a hsv image has three channels and is shape n rows n cols 3 the hue channel is well suited for color detection tasks because we can filter by color on a single dimension of measurement and it is a measure that is invariant to lighting conditions opencv provides methods to convert images from one color space to another http docs opencv org 2 4 modules imgproc doc miscellaneous transformations html cvtcolor a good first step would be convert self cv image into an hsv image and visualize it like any good roboticist visualize everything to make sure it meets your expectations note if you are using opencv 3 1 which is the case for anyone on kinetic and ubuntu 16 04 make sure to never called cv2 imshow from one of your sensor callback threads you should only ever call it from the main thread filtering the image for only yellow since the set of signs we are recognizing are all yellow by design we can handtune a filter that will only select the certain shade of yellow in our image here s a callback that will help to display the rgb value when hovering over the image window with a mouse note you get this behavior for free with opencv 3 1 python def process mouse event self event x y flags param process mouse events so that you can see the color values associated with a particular pixel in the camera images self image info window 255 np ones 500 500 3 show hsv values cv2 puttext self image info window color h d s d v d self hsv image y x 0 self hsv image y x 1 self hsv image y x 2 5 50 5 x 50 y cv2 font hershey simplex 1 0 0 0 show bgr values cv2 puttext self image info window color b d g d r d self cv image y x 0 self cv image y x 1 self cv image y x 2 5 100 cv2 font hershey simplex 1 0 0 0 in the init method connect this callback by adding the following line python self image info window none cv2 setmousecallback video window self process mouse event and add the following lines to your run loop python if not self image info window is none cv2 imshow image info self image info window cv2 waitkey 5 now if you hover over a certain part of the image it will tell you what r g b value you are hovering over once you have created an hsv image you can edit this function to also display the hue saturation and value numbers opencv windows can be pretty powerful when setting up interactive sliders to change parameters as stated in class for neato soccer if you want to learn dynamic reconfigure you can use that instead of opencv s trackbars in the init method copy the following lines which python cv2 namedwindow threshold image self hsv lb np array 0 0 0 hsv lower bound cv2 createtrackbar h lb threshold image 0 255 self set h lb cv2 createtrackbar s lb threshold image 0 255 self set s lb cv2 createtrackbar v lb threshold image 0 255 self set v lb self hsv ub np array 255 255 255 hsv upper bound cv2 createtrackbar h ub threshold image 0 255 self set h ub cv2 createtrackbar s ub threshold image 0 255 self set s ub cv2 createtrackbar v ub threshold image 0 255 self set v ub then add the following callback methods to the class definition that respond to changes in the trackbar sliders python def set h lb self val set hue lower bound self hsv lb 0 val def set s lb self val set saturation lower bound self hsv lb 1 val def set v lb self val set value lower bound self hsv lb 2 val def set h ub self val set hue upper bound self hsv ub 0 val def set s ub self val set saturation upper bound self hsv ub 1 val def set v ub self val set value upper bound self hsv ub 2 val the sliders will help set the hsv lower and upper bound limits self hsv lb and self hsv ub which you can then use as limits for filtering certain parts of the hsv spectrum check out the opencv inrange method http opencv python tutroals readthedocs io en latest py tutorials py imgproc py colorspaces py colorspaces html for more details on how to threshold an image for a range of a particular color by the end of this step you should have a binary image mask where all the pixels that are white represent the color range that was specified in the thresholding operation generating a bounding box you can develop an algorithm that operates on the binary image mask that you developed in the step above one method that could be fruitful would be dividing the image in a grid you might want to write a method that divides the image into a binary grid of grid size m n if tile in the grid contains a large enough percentage of white pixels the tile will be turned on since the images are stored as 2d arrays you can use numpy like syntax to slice the images in order to obtain these grid cells we ve provided an example in grid image py which i ll show here python import cv2 import os imgpath os path join os path dirname os path realpath file images leftturn scene jpg img cv2 imread imgpath grid cell w 64 3 grid cell h 48 3 cv2 namedwindow my window numpy array slicing grid cell img grid cell h 2 grid cell h grid cell w 2 grid cell w cv2 imshow my window grid cell cv2 waitkey 0 the task now is to decide which grid cells contain the region of interest you can write another function that takes this binary grid and determines the bounding box that will include all the grid cells that were turned on grid grid images grid png opencv has a method called boundingrect which seems promising too i found a nice example albeit in c that finds a bounding rectangle using a threshold mask http answers opencv org question 4183 what is the best way to find bounding box for binary mask like you have it seems like it will depend on your thresholding operation to be pretty clean i e no spurious white points the only object that should be unmasked is the sign of interest boundingrectstars boundingrectstars images boundingrectstars png the goal is to produce left top x1 y1 and right bottom x2 y2 that define the top left and bottom right corners of the bounding box recognition recognizing the signs involves determining how well the cropped image if the sign matches the template image for each type of sign to do this we will find keypoints in the template image and in the input image then see how well we can align the keypoints and finally see how similar the aligned images are testing we have template images as well as static test images for the template matcher in the repository so we can run the code with the correct template images and try to match them to the static test images we have to do this first we need to initialize the template matcher with the template images python if name main images left images leftturn box small png right images rightturn box small png uturn images uturn box small png tm templatematcher images you can put this at the bottom of the file and this if statement will mean that this part won t run when template matcher is imported by other files next we can run tm predict on our test scenes using this python scenes images uturn scene jpg images leftturn scene jpg images rightturn scene jpg for filename in scenes scene img cv2 imread filename 0 pred tm predict scene img print filename split 1 print pred this reads the test images runs tm predict on each image and prints the file name followed by the prediction given just the starter code this should be the output bash uturn scene jpg leftturn scene jpg rightturn scene jpg finding keypoints we are finding keypoints using open cv s implementation of the sift algorithm http docs opencv org 3 1 0 da df5 tutorial py sift intro html then filtering the keypoints ourselves to find the points that match between the input image and each template for the template images we can calculate the keypoints in the initialization function because the images won t chage to find those keypoints we can cycle through the input dictionary of template images read the image files as grayscale images and compute the keypoints using opencv s sift implementation python for k filename in images iteritems load template sign images as grayscale self signs k cv2 imread filename 0 precompute keypoints and descriptors for the template sign self kps k self descs k self sift detectandcompute self signs k none the predict method is the main method of templatematcher it begins by finding the keypoints in the input image as the first step to matching it with at template at this point the template images are initialized and the predict method should run so you can run the program and it should return predictions for each template image with a zero confidence value next predict calls compute prediction to find how well the input image matches each template image and stores the predictions in a dictionary aligning keypoints in compute prediction the first step to aligning the images is to find which keypoints from the two images match the simplest method for that is to say keypoints whcih are close to eachother match note later steps will correct for matched keypionts from mismatched images based on the matched keypoints the following lines find how to transform the input image so the keypoints align with the template images keypoints using a homography matrix then transorms the input image using that matrix so it should align with the template python transform input image so that it matches the template image as well as possible m mask cv2 findhomography img pts template pts cv2 ransac self ransac thresh img t cv2 warpperspective img m self signs k shape 1 once you add these lines you should change img in the line visual diff compare images img self signs k to img t comparing images at this point we have two images which if they are of the same sign should be aligned with each other if they are of different signs the matched keypoints were likely not well aligned and the homography matrix probably skewed the image into an unrecognizable blob but the computer can t tell what is a reasonable image and what is an unrecognizable blob so now we have to determine how similar the two images are the compare images function at the bottom of the file is used to find how similar two images are to each other this one is left up to you but here are a few hints first there is one thing we have yet to account for while comparing images lighting if you have tried to do blob detection and then tried again when the sun went down you know that lighting wreaks havoc on computer vision since we are using grayscale images and we have cropped them so both images are of the same thing we can eliminate most of the effects of lighting by normalizing the image mathematically this can be done by taking each element mean standard dev images are stored as numpy arrays so you can use some nice numpy functions to make this math easier for finding the difference between the images remember that in code an image is just an array of numbers you can do arithmatic with the images to find how close they are to the same converting to useful output back in the predict method the output of compare images passes through compute prediction and is saved in the dictionary visual diff which maps the keys associated with the template images to the calulated difference between that template image and the input image the final step for templatematcher is to convert these differences into a scaled confidence value representing how well the input image matches each of the templates this step is really just algebra you need to make large numbers small and small numbers large but you also want to scale your output so that a value near 1 always represents a high confidence conclusion that s all this class now outputs how well an input image matches each of a given set of templates one nice part about this approach is that no single step needs to be perfectly tuned finding slightly too many keypoints initially is quickly corrected when you find matches and incorrect matches are generally eliminated in the homography matrix transformation so by the time you get to the numerical comparison of images you are usually looking at either a reasonable match or two extremely different images this system is therefore relatively robust and has a low rate of false positives however it is suseptible to false negatives navigating | ai |
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FreeRTOS-ESP-IDF-HTTP-Client | http esp idf esp idf http client examples of rest api for esp32 proj4 get request example proj5 post request example | os |
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prompt4bli | prompt4bli this repository is the official pytorch implementation of the following paper yaoyiran li anna korhonen and ivan vuli 2023 on bilingual lexicon induction with large language models in proceedings of the 2023 conference on empirical methods in natural language processing emnlp 2023 arxiv https arxiv org abs 2310 13995 openreview https openreview net group id emnlp 2023 conference prompt4bli aims to address the bilingual lexicon induction bli word translation tasks with autoregressive large language models llms we for the first time demonstrate that prompting multilingual llms for bli outperforms traditional bli approaches which rely on calculating cross lingual word embeddings clwes while we show that prompting off the shelf llms can already establish new state of the art bli performance on many bli language pairs our main experimental setup the prompt4bli repo also provides code for bli oriented fine tuning which can further improve the results as a side experiment demonstrated on smaller scale llms traditional methods rely on learning parameterized clwe mappings or cross lingual word pair scoring functions and usually tackle bli in three setups 1 supervised 5k seed translation pairs 2 semi supervised 1k seed translation pairs 3 unsupervised 0 seed translation pairs cf our previous work contrastivebli https github com cambridgeltl contrastivebli and blicer https github com cambridgeltl blicer different from traditional methods prompt4bli only makes use of off the shelf llms not requiring llm fine tuning nor updating any learnable parameters our work considers the following prompting setups few shot prompting we propose to retrieve a subset of the seed translation pairs nearest neighbour retrieval as in context examples for prompting corresponds to the traditional supervised and semi supervised bli setups where the seed bilingual dictionary size is 5k and 1k respectively zero shot prompting no in context examples are used corresponds to the traditional unsupervised bli setup dependencies pytorch 1 10 1 transformers 4 28 1 llms used in our work llm hugging face model id mt5 small google mt5 small mt5 base google mt5 base mt5 large google mt5 large mt5 xl google mt5 xl mt5 xxl google mt5 xxl mt0 small bigscience mt0 small mt0 base bigscience mt0 base mt0 large bigscience mt0 large mt0 xl bigscience mt0 xl mt0 xxl bigscience mt0 xxl xglm 564m facebook xglm 564m xglm 1 7b facebook xglm 1 7b xglm 2 9b facebook xglm 2 9b xglm 4 5b facebook xglm 4 5b xglm 7 5b facebook xglm 7 5b mgpt sberbank ai mgpt llama 7b huggyllama llama 7b llama 13b huggyllama llama 13b data following contrastivebli https github com cambridgeltl contrastivebli and blicer https github com cambridgeltl blicer our data is obtained from the xling https github com codogogo xling eval 8 languages 56 bli directions in total and panlex bli https github com cambridgeltl panlex bli 15 lower resource languages 210 bli directions in total get xling data bash sh get xling data sh for panlex bli please see get panlex data get panlex data where we provide the code for deriving the monolingual word embeddings run the code prepare bli data and extract in context examples for few shot prompting xling bash python run extract vocabularies py python run extract bli data py prepare bli data and extract in context examples for few shot prompting panlex bli bash python run extract vocabularies panlex py python run extract bli data panlex py optional run bli oriented llm fine tuning define llm dirs learning rate batch size and random seed in run training py bash python run training py run bli evaluation define seed dictionary size n shot llm dir and language pairs to evaluate manually in run bli py bash python run bli py citation please cite our paper if you find prompt4bli useful bibtex inproceedings li etal 2023 on title on bilingual lexicon induction with large language models author li yaoyiran and korhonen anna and vuli c ivan booktitle proceedings of the 2023 conference on empirical methods in natural language processing year 2023 | bilingual-dictionary-induction bilingual-lexicon-extraction bilingual-lexicon-induction large-language-models llms machine-translation multilingual-models multilingual-nlp word-translation low-resource-machine-translation pytorch prompt prompt-engineering prompting prompts llama mt5 | ai |
ml-workshop-4-of-4 | advanced machine learning with scikit learn imbalanced classification and text data part 4 of 4 other parts part 1 https github com amueller ml workshop 1 of 4 part 2 https github com amueller ml workshop 2 of 4 part 3 https github com amueller ml workshop 3 of 4 content working with imbalanced data https amueller github io ml workshop 4 of 4 slides 01 imbalanced data html feature selection https amueller github io ml workshop 4 of 4 slides 02 feature selection html working with text data https amueller github io ml workshop 4 of 4 slides 03 working with text data html building custom estimators and extending scikit learn https github com amueller ml workshop 4 of 4 blob master notebooks 04 20custom 20estimators ipynb instructor andreas mueller http amuller github io amuellerml https twitter com amuellerml columbia university book introduction to machine learning with python http shop oreilly com product 0636920030515 do this repository will contain the teaching material and other info associated with the workshop advanced machine learning with scikit learn part ii ii please download the large movie review dataset from http ai stanford edu amaas data sentiment before coming to the workshop about the workshop scikit learn is a machine learning library in python that has become a valuable tool for many data science practitioners this training will cover some advanced topics in using scikit learn and how to build your own models or feature extraction methods that are compatible with scikit learn we will also discuss different approaches to feature selection and resampling methods for imbalanced data finally we ll discuss how to do classification of text data using the bag of words model and its variants prerequisites this workshop assumes familiarity with jupyter notebooks and basics of pandas matplotlib and numpy it also assumes experience using scikit learn and familiarity with the api obtaining the tutorial material if you are familiar with git it is most convenient if you clone the github repository this is highly encouraged as it allows you to easily synchronize any changes to the material git clone https github com amueller ml workshop 4 of 4 if you are not familiar with git you can download the repository as a zip file by heading over to the github repository https github com amueller ml workshop 4 of 4 in your browser and click the green download button in the upper right images download repo png please note that i may add and improve the material until shortly before the tutorial session and we recommend you to update your copy of the materials one day before the tutorials if you have an github account and forked cloned the repository via github you can sync your existing fork with via the following commands git pull origin master installation notes this tutorial will require recent installations of numpy http www numpy org scipy http www scipy org matplotlib http matplotlib org pillow https python pillow org pandas http pandas pydata org scikit learn http scikit learn org stable 0 22 1 ipython http ipython readthedocs org en stable jupyter notebook http jupyter org mlxtend imbalance learn the last one is important you should be able to type jupyter notebook in your terminal window and see the notebook panel load in your web browser try opening and running a notebook from the material to see check that it works for users who do not yet have these packages installed a relatively painless way to install all the requirements is to use a python distribution such as anaconda https www continuum io downloads which includes the most relevant python packages for science math engineering and data analysis anaconda can be downloaded and installed for free including commercial use and redistribution the code examples in this tutorial requires python 3 5 or later after obtaining the material we strongly recommend you to open and execute a jupyter notebook jupter notebook check env ipynb that is located at the top level of this repository inside the repository you can open the notebook by executing bash jupyter notebook check env ipynb inside this repository inside the notebook you can run the code cell by clicking on the run cells button as illustrated in the figure below images check env 1 png finally if your environment satisfies the requirements for the tutorials the executed code cell will produce an output message as shown below images check env 2 png | ai |
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Chefyyy | p align center h1 align center chefyyy h1 p p align center chefyyy is a mobile application that will suggest food recipes p p align center img src https graduate edu lk wp content uploads 2020 05 iit logo png height 80 p | front_end |
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blockchain-configuration-files | blocknet s blockchain configuration files this repository contains the blockchain configuration files needed for trustless exchange xbridge on the blocknet protocol an overview of the file structure and contents directions on how to create configuration files from scratch if you need any assistance with this please contact us in the block dx listing https discord gg wnaygwwqfy channel of the blocknet discord server directions on how to setup configuration files for use of the blocknet protocol directions on how to test a blockchain for compatibility with xbridge website https blocknet co api https api blocknet co documentation https docs blocknet co discord https discord gg wnaygwwqfy table of contents update with doctoc in the readme md directory doctoc repo https github com thlorenz doctoc blob master readme md start doctoc generated toc please keep comment here to allow auto update don t edit this section instead re run doctoc to update blocknet s blockchain configuration files blocknets blockchain configuration files table of contents table of contents major update to workflow major update to workflow configuration file overview configuration file overview manifest file manifest file xbridge and wallet configuration files xbridge and wallet configuration files xbridge configuration files xbridge configuration files wallet configuration files wallet configuration files creating configuration files creating configuration files creating the coin base j2 file creating the coinbasej2 file setup servicenode or trading node configuration files setup servicenode or trading node configuration files setup xbridge conf setup xbridgeconf setup wallet files setup wallet files testing a blockchain for compatibility testing a blockchain for compatibility successful exchange successful exchange contributing contributing end doctoc generated toc please keep comment here to allow auto update major update to workflow the coin update and addition process has been overhauled as of november 2021 summary documentation regarding the entire workflow is available in autobuild workflow md autobuild workflow md detailed information about the coin configuration information is presented here configuration file overview the data structure of the configuration files is designed to accommodate for versioning there are several data sets as follows manifest latest json contains the latest official master data and versioning reference of each blockchain autobuild configs coin base j2 contains the jinja base template file for each blockchain autobuild templates wallet conf j2 contains the jinja base wallet template file autobuild templates xbridge conf j2 contains the jinja base xbridge template file wallet confs contains the versioned wallet configuration files for each blockchain generated from the manifest config base and wallet template files xbridge confs contains the versioned xbridge configuration files for each blockchain generated from the manifest config base and xbridge template files manifests contains archived manifest versions that align with blocknet wallet releases manifest json deprecated manifest file the manifest latest json contains an array of objects with each object containing versioning information for each supported blockchain the format of each object is as follows blockchain ticker ver id ver name conf name daemon stem dir name linux dir name mac dir name win repo url versions xbridge conf wallet conf all entries are mandatory unless otherwise noted key description blockchain the name of the blockchain blocknet dogecoin syscoin etc this is user facing case sensitive and should remain consistent across all version groups for this blockchain ticker the abbreviation used to represent this blockchain on exchanges block doge sys etc use all uppercase letters ver id this id must be unique not change and is case sensitive the format used is the wallet s configuration file name without the extension followed by a double hyphen then the initial wallet version added to this compatibility versioning group blocknet v4 2 0 dogecoin v1 10 0 dogeparty syscoin 3 0 5 0 etc ver name user viewable and user friendly name for the blockchain version group this can be changed renamed and is used to indicate the blockchain versioning group for example blocknet s current ver name is blocknet v4 if a new version group was created for the next wallet version release the ver name could be changed to blocknet v4 v4 4 0 conf name the name of the wallet s configuration file with the extension blocknet conf dogecoin conf syscoin conf etc this is case sensitive and should be written exactly as it is in the file name daemon stem optional a standard executable name is constructed by appending d to the conf name with the extension removed eg the executable name for blocknet is blocknetd constructed from the blocknet of blocknet conf minus the extension and with a d appended if the coin has a non standard executable name eg sccd for stakecubecoin verged for verge specify the stem here eg scc for stakecubecoin dir name linux this is the name of the blockchain s folder in the data directory blocknet dogecoin syscoincore etc this is case sensitive and should be written exactly as it is in the folder name dir name mac this is the name of the blockchain s folder in the data directory blocknet dogecoin syscoincore etc this is case sensitive and should be written exactly as it is in the folder name dir name win this is the name of the blockchain s folder in the data directory blocknet dogecoin syscoincore etc this is case sensitive and should be written exactly as it is in the folder name repo url this is the url of the wallet s github repository https github com blocknetdx blocknet https github com blocknetdx blocknet https github com dogecoin dogecoin https github com dogecoin dogecoin https github com syscoin syscoin https github com syscoin syscoin etc do not include trailing slashes versions this is an array of wallet versions compatible with the referenced xbridge conf and wallet conf configuration files the versioning used is case sensitive and must be exactly the same as tagged in the github repo for each release v4 3 0 https github com blocknetdx blockdx releases tag v4 3 0 blocknet v1 10 0 dogeparty https github com dogecoin dogecoin releases tag v1 10 0 dogeparty dogecoin 3 0 5 0 https github com syscoin syscoin releases tag 3 0 5 0 syscoin etc xbridge conf this is the name of the xbridge configuration file within the xbridge confs folder the title of this file should be the same as the ver id in all lowercase wallet conf this is the name of the wallet configuration file within the wallet confs folder the title of this file should be the same as the ver id in all lowercase example manifest object blockchain blocknet ticker block ver id blocknet v4 2 0 ver name blocknet v4 conf name blocknet conf dir name linux blocknet dir name mac blocknet dir name win blocknet repo url https github com blocknetdx blocknet versions v4 2 0 v4 3 0 v4 3 1 v4 3 2 v4 3 3 xbridge conf blocknet v4 2 0 conf wallet conf blocknet v4 2 0 conf xbridge and wallet configuration files these were formerly primary configuration objects but are now derived from the jinja coin base j2 file and xbridge and wallet templates although they are no longer the primary configuration objects in the xbridge configuration space they remain for the time being the primary blockdx configuration elements shown below are the format and examples of xbridge and wallet configuration files the format and examples of the jinja file from which they are generated the jinja files are generated by the app py autobuild app py python script xbridge configuration files the xbridge configuration files are versioned for each blockchain using the manifest file as a key the format of each file is as follows ticker ticker used to represent the blockchain on exchanges title name of the blockchain with spaces removed address deprecated can be left blank ip 127 0 0 1 client ip localhost or ip of machine vm vps port wallet rpc port listed in the wallet conf username wallet rpc username created in the wallet conf password wallet rpc password created in the wallet conf addressprefix decoded public address base58 hex 0 2 decimal scriptprefix decoded p2sh address base58 hex 0 2 decimal secretprefix decoded private key base58 hex 0 2 decimal coin coin precision defined in source code coin minimumamount 0 minimum exchange amount leave value set to 0 txversion the version value found in raw transactions dustamount 0 leave value set to 0 createtxmethod type of transaction method btc sys dgb etc getnewkeysupported true leave value set to true importwithnoscansupported true leave value set to true mintxfee 0 leave value set to 0 adjust fee in feeperbyte blocktime the blockchain s block time in seconds feeperbyte the fee per byte to send a transaction confirmations 0 number of confirmations to complete exchange example xbridge file file name blocknet v4 2 0 conf block title blocknet address ip 127 0 0 1 port 41414 username password addressprefix 26 scriptprefix 28 secretprefix 154 coin 100000000 minimumamount 0 txversion 1 dustamount 0 createtxmethod btc getnewkeysupported true importwithnoscansupported true mintxfee 10000 blocktime 60 feeperbyte 20 confirmations 0 the file name should match the value of the manifest file s xbridge conf key for the version group that these xbridge values pertain to wallet configuration files the wallet configuration files are versioned for each blockchain using the manifest latest json file as a key the format of each file is as follows server 1 enable disable command line and json rpc commands listen 1 enable disable peer connections rpcuser custom username must match in xbridge conf rpcpassword custom password must match in xbridge conf rpcallowip 127 0 0 1 client ip localhost or ip of machine vm vps port port to listen for connections on rpcport port to listen for json rpc connections on txindex 1 transaction index is required for getrawtransaction rpc call addresstype legacy required for segwit activated blockchain changetype legacy required for segwit activated blockchain deprecatedrpc custom content depending on the coin example wallet file file name blocknet v4 2 0 conf server 1 listen 1 rpcuser rpcpassword rpcallowip 0 0 0 0 0 port 41412 rpcport 41414 txindex 1 the file name should match the value of the manifest file s wallet conf key for the version group that these xbridge values pertain to creating configuration files you need to create a coin base j2 template in the autobuild configs directory it is essentially a concatenation of the xbridge and wallet configuration files with some special treatment for substitution variables in the automation workflows supporting multiple versions certain wallet directives in the following explanation blocknet wallet v4 3 0 https github com blocknetdx blockdx releases tag v4 3 0 will be used as an example throughout the process of creating a jinja template configuration file the name of this file should be the concatenation of the lowercase version of the ticker and base j2 blocknet example ticker block yields block base j2 the current blocknet block base j2 looks like this block title blocknet address ip 127 0 0 1 rpcport rpcport default 41414 p2pport p2pport default 41412 username rpcusername password rpcpassword addressprefix 26 scriptprefix 28 secretprefix 154 coin 100000000 minimumamount 0 dustamount 0 createtxmethod btc getnewkeysupported true importwithnoscansupported true mintxfee 10000 blocktime 60 txversion 1 feeperbyte 20 confirmations 0 versions v4 2 0 legacy false deprecatedrpc false xbridge conf blocknet v4 2 0 conf wallet conf blocknet v4 2 0 conf getnewkeysupported false v4 3 0 legacy false deprecatedrpc false xbridge conf blocknet v4 2 0 conf wallet conf blocknet v4 2 0 conf getnewkeysupported false v4 3 1 legacy false deprecatedrpc false xbridge conf blocknet v4 2 0 conf wallet conf blocknet v4 2 0 conf getnewkeysupported false v4 3 2 legacy false deprecatedrpc false xbridge conf blocknet v4 2 0 conf wallet conf blocknet v4 2 0 conf getnewkeysupported false v4 3 3 legacy false deprecatedrpc false xbridge conf blocknet v4 2 0 conf wallet conf blocknet v4 2 0 conf getnewkeysupported false creating the coin base j2 file the file is a json format list starting with the coin name coin the name is followed by the fields which comprise the xbridge config fragment for the coin directive comment title blocknet the title is the name of the blockchain with the spaces removed for blocknet it s simply blocknet address this address setting is not currently used and can be left blank ip 127 0 0 1 this is the ip of the client wallet default should be set to 127 0 0 1 and if needed can be changed by the end user when setting up the configuration files for the given environment port rpcport default 41414 this is a placeholder for the rpc port for the wallet and a default value most qt wallets will list a default suggested rpc port under help command line options server options in here will be a line similar to the following br listen for json rpc connections on port default 41414 or testnet 41419 br as shown the rpc port is 41414 if this port is used by another blockchain an arbitrary available port may be used username rpcusername substitution value to be supplied by workflow automation password rpcpassword substitution value to be supplied by workflow automation addressprefix 26 there are multiple steps to obtain the address prefix br 1 copy a public address ie bue65eaeh3nzwnm2p5yksstxteyvshi4yu br 2 visit this online base58 tool lenschulwitz com http lenschulwitz com base58 br 3 paste the address in the bitcoin address base58 decoder input and decode the address to a hex string 1a04cdffba976b79c0d25f06c56151fef6a2a3156bd60f2398 br 4 copy the first 2 characters of the hex string 1a br 5 visit this online hex converter tool binaryhexconverter com https www binaryhexconverter com hex to decimal converter br 6 paste the 2 characters in the hex value input and convert it to decimal value 26 br 7 the address prefix is 26 scriptprefix 28 the script prefix follows the same process as the address prefix but with a p2sh public address instead of a regular public address follow these steps to obtain the p2sh public address and prefix br 1 open the wallet s debug console br 2 type validateaddress pub address replacing pub address with a public address and press enter br 3 from the json output copy the value of pubkey br 4 in the console type decodescript pubkey output replacing pubkey output with the value copied in the previous step and press enter br 5 from the json output copy the value of p2sh which is the p2sh public address br 6 perform the steps provided for addressprefix using this p2sh public address secretprefix 154 the secret prefix follows the same process as the address prefix also but with a private key instead of a public address follow these steps to obtain the private key and prefix br 1 open the wallet s debug console br 2 type dumpprivkey pub address replacing pub address with a public address and press enter br 3 copy the console output which is the private key br 4 perform the steps provided for addressprefix using this private key coin 100000000 coin precision determined in source code coin 100000000 1 00000000 coin 1000000 1 000000 minimumamount 0 this is the minimum exchange amount keep this value set to 0 txversion 1 the transaction version is the value of version in raw transactions there are two ways this value can be found br method 1 br 1 check if the blockchain explorer has raw transaction json in the transaction detail pages for blocknet this can be found in the explorer raw transaction tab https chainz cryptoid info block tx dws 274413 htm br 2 in the json the version can be found as the second key value pair version 1 br method 2 br 1 alternatively the transaction version number can be found using a wallet s debug console br 2 open the wallet s debug console br type getrawtransaction tx id replacing tx id with a transaction id hash 060f838a8df0e089350834c1ef541418f2f9e1bca952bdcc0f4dbe64af2188c6 br 3 copy the console output which is a hex string that needs to be decoded br 4 in the console type decoderawtransaction hex string replacing hex string with the outputted hex string br 5 in the json output the version can be found as the second key value pair version 1 dustamount 0 this specifies the dust amount threshold keep this value set to 0 createtxmethod btc the transaction method refers to the type of transaction procedure that is used for the blockchain the different types can be found by looking at the variations of xbridgewalletconnector files in the blocknet github https github com blocknetdx blockdx tree master src xbridge such as btc dgb or sys example xbridgewalletconnectordgb cpp getnewkeysupported true keep this value set to true importwithnoscansupported true keep this value set to true mintxfee 0 this can be used to set a minimum transaction fee but most of the time it works best to keep this value set to 0 and instead adjust the value of feeperbyte blocktime 60 this is the blockchain s block time in seconds which is usually readily available for blocknet it s 60 seconds if this is not available then a close estimate can be calculated in two ways br method one br 1 visit the explorer and expand the list to view the last 500 blocks br 2 record how long ago the time of the 500th block was or the difference in time between the most recent block and the 500th block 3 the block time would be the time in seconds divided by 500 blocktime time in seconds 500 br method two br 1 open the wallet s debug console br 2 type getmininginfo and press enter br 3 from the json output copy the values of difficulty and networkhashps br 4 the block time would be 2 32 times the difficulty divided by the network hashrate blocktime 2 32 difficulty networkhashps feeperbyte 20 the fee per byte is how much to charge per byte for an exchange this can be calculated by looking in the wallet send function for the recommended fee per byte and then multiplying it by 2 2 5 since there s 2 transactions that take place in an exchange one transaction from one party s address to the p2sh address and then a second transaction from the p2sh address to the counterparty s address confirmations 0 confirmations is the minimum amount of transaction confirmations required until funds are spent and an exchange is complete requiring more confirmations increases the time an exchange takes but makes it more secure as the network verifies the data by default the number of confirmations required is set to 0 versions contains a list of configuration sections for each supported version in this ver id each section may have different values depending on the coin version the format of each section is shown below directive comment vx y z a specific version number legacy false blockdx does not yet support segwit because segwit addresses start with the same prefix as p2sh addresses this key should be set to true for coins which support segwit in this case the wallet config will be generated with the following directives which instruct the wallet to use legacy non p2sh addresses br addresstype legacy br changetype legacy deprecatedrpc false some coin wallets have deprecated or changed the output format of some rpc replies it may be possible and perhaps necessary for correct xbridge operation to restore the old behaviour setting this value to true will generate the wallet config with the following directive br deprecatedrpc addresses br alternatively freeform text may be specified in which case the wallet config will be generated with a line containing br deprecatedrpc your freeform text br for example digibyte requires br deprecatedrpc signrawtransaction xbridge conf blocknet v4 2 0 conf the name of the xbridge file must be of the form lower case coin name version conf wallet conf blocknet v4 2 0 conf the name of the wallet file must be of the form lower case coin name version conf getnewkeysupported false leave this set to false the file finishes with closing parentheses setup servicenode or trading node configuration files the xbridge and wallet configuration files in this repo are named and organized for versioning in order to properly set up an environment for use with the blocknet protocol s xbridge component additional steps must be taken the following instructions describe the legacy manual approach we are migrating to a more automated docker based approach and that workflow is described in the blocknet doc portal https docs blocknet co service nodes setup setup xbridge conf the xbridge conf is a single file includes a main heading followed by a newline followed by the contents of all the individual xbridge configuration file of any blockchain being used with each one separated by a newline heading format main exchangewallets the heading must be at the top of the file the leading line main does not change the exchangewallets setting is used to list the blockchains to be supported in other words any blockchain that will be used in an exchange must be listed here if running a service node only the blockchains that the node will support should be listed here the values taken are the blockchain ticker values from the individual xbridge configuration file heading example main exchangewallets btc sys block dgb qtum dash xzc bitg ltc doge pivx xsn mona via lbc after the heading the contents of the individual xbridge configuration files of the blockchains listed under exchangewallets are listed to find the proper xbridge settings for each blockchain first find the version group in the manifest file for each blockchain that has the wallet version to be used listed in the versions array if a version is not listed then it is not yet supported copy the contents of each file and paste it into the xbridge conf file for an example of what a complete and properly formatted xbridge conf file looks like take a look at the example xbridge conf file in this repo make sure to update the following for each configuration entry setting procedure ip 127 0 0 1 this is the ip of the client wallet and must be the same as the value for rpcallowip in the wallet configuration file in most cases this can be left as localhost 127 0 0 1 but may need an another ip if using a vm vps or some other non local setup username blockhead the username is made up and the value must be the same as created in the wallet configuration file password fa506e4a01a7 the password is made up and the value must be the same as created in the wallet configuration file setup wallet files each blockchain wallet installation has a configuration file for blocknet it is blocknet conf these contents are to be replaced by the contents of the wallet configuration file referenced in the respective manifest latest json version group to find the proper wallet configuration for each blockchain first find the version group in the manifest latest json file for each blockchain that has the wallet version to be used listed in the versions array if a version is not listed then it is not yet supported copy the contents of each referenced wallet configuration file and paste it into the configuration file of the downloaded wallet make sure to update the following for each configuration entry setting procedure rpcallowip 127 0 0 1 this is the ip of the client wallet and must be the same as the value for ip in the xbridge conf entry for this blockchain in most cases this can be left as localhost 127 0 0 1 but may need an another ip if using a vm vps or some other non local setup rpcuser blockhead the username is made up and the value must be the same as created in the xbridge conf entry for this blockchain rpcpassword fa506e4a01a7 the password is made up and the value must be the same as created in the xbridge conf entry for this blockchain testing a blockchain for compatibility to attempt an exchange to test compatibility the following will be needed a small amount above dust value of the blockchain s token blocknet s token block for the exchange network fee and token of a compatible blockchain to trade against for simplicity it is recommended you perform your test trades against block depending on the token s unit value 1 3 usd should work fine run the full nodes for each of the blockchains being tested on a testnet service node with the composed configuration files contact a contributor for tblock or see if the chains can be added to existing testnet service nodes the preferred way to do this is through the block dx listing https discord gg wnaygwwqfy channel in the blocknet discord server two instances of each blockchain used in testing must be run with each having the respective configuration files exchanges cannot be taken by the client that created them make sure debug 1 is set in the blocknet conf file to receive full debug logs if there s an issue once the test environment is ready create an order on one test machine and take it from the other if everything is correct the exchange will succeed if the exchange fails you should find clues as to the reason in the xbridge p2p logs or possibly in the coin debug log s the xbridge p2p logs are located in the blocknet datadir log xbridgep2p yyyymmdd log these can be difficult to read so there is a handy script to extract and format all the messages relating to a particular order id parse xbridge py tools parse xbridge py it requires one parameter the name of the log file to search when run it asks for the order id and extracts and formats all the records related to that order successful exchange if the exchange is successful there is still a little more testing to do interrupt an in progress trade for example by disconnecting one blockdx instance from the internet for a few minutes and check that it rolls back properly and all funds end up back in their starting posistions depending on the actual point you interrupt the trade this could take anywhere from about 30 minutes to 2 hours when all testing is successful a commit should be made to the respective configuration files the commit comments should include the successful order ids and a summary of the tests conducted there are a few variations of scenarios new wallet version existing blockchain existing configurations existing version group br if a successful exchange was confirmed for a new wallet version using the same xbridge conf and wallet conf listed under a single existing manifest version group then all that is needed is to update that version group versions array in the manifest latest json and coin base j2 files with the new wallet version as is listed in the tag of the github release your commit should include just these two files manifest latest json autobuild configs lowercase coin name base j2 new wallet version existing blockchain existing configurations new version group br if a new wallet version is successful using xbridge conf and wallet conf from two different manifest version groups then a new version group must be created this new version group would have this new wallet as the only version listed under versions as is listed in the tag of the github release with a new ver id the respective xbridge and wallet configuration files used listed for xbridge conf and wallet conf and any other changes that may be required your commit will include just these two files manifest latest json autobuild configs lowercase coin name base j2 new wallet version existing blockchain new configurations new version group br if a new wallet version is successful using a newly generated xbridge or wallet configuration file then a new manifest version group must be created as well as the new configuration file this new version group would have this new wallet as the only version listed under versions as is listed in the tag of the github release with a new ver id the respective xbridge and wallet configuration files used listed for xbridge conf and wallet conf and any other changes that may be required your commit will include the first two and at least one of the last two of these files manifest latest json autobuild configs lowercase coin name base j2 wallet files lower case coin name version conf xbridge files lower case coin name version conf new blockchain new configurations new version group br if a successful exchange was confirmed for a new blockchain then a new manifest version group must be created as well as the new configuration files your commit will include four files manifest latest json autobuild configs lowercase coin name base j2 wallet files lower case coin name version conf xbridge files lower case coin name version conf contributing want to help build the internet of blockchains check out our contributing documentation https github com blocknetdx blocknet blob master contributing md | blockchain |
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bzion | p align center img alt bzion logo src github bzion png p build status https travis ci org allejo bzion png branch master https travis ci org allejo bzion scrutinizer code quality https scrutinizer ci com g allejo bzion badges quality score png s 291afbdf9d3ff68b2e2f44e9d02533795bcbf107 https scrutinizer ci com g allejo bzion code coverage https scrutinizer ci com g allejo bzion badges coverage png b master https scrutinizer ci com g allejo bzion code structure master elementtype class orderfield test coverage order asc changesexpanded 0 dependency status https www versioneye com user projects 59b30b61368b08003d0e846d badge svg https www versioneye com user projects 59b30b61368b08003d0e846d bzion is a modern league management system lms written for bzflag leagues to manage players teams matches and tournaments project maintainers these individuals are charged with maintaining and leading the project vladimir jimenez allejo https github com allejo konstantinos kanavouras kongr45gpen alezakos https github com kongr45gpen contributors these individuals have made multiple significant contributions to the project on a sustained basis they become actively involved with improving and adding new features to the project matthew pavia tw1sted https github com mattpavia ashvala vinay ashvala https github com ashvala special thanks to these individuals have assisted significantly with guiding the project in its current direction and have contributed several suggestions to continuously improve the project blast007 https github com blast007 constitution https github com macsforme installation the installation process is documented on the bzion wiki https github com allejo bzion wiki installation and the requirements https github com allejo bzion wiki installation requirements for installation are documented as well if you re having issues during your installation common issues are also documented on the wiki https github com allejo bzion wiki installation troubleshooting as well as with any system you are recommended to take regular backups of your database license gnu general public license 3 0 https github com allejo bzion blob master license md | league-management tournaments bzflag gaming web symfony symfony-application | os |
RTOS | rtos real time operating system when it matters it runs on rtos realtime computing myth v s truth myth rtc real time computing is fast computing with higher performance truth rtc is predictable computing timeliness is more important than perfomance rtos deals with gurantees not with raw speed having more processor more ram faster bus interfaces does t make a system real time it deals with guarantees p align center img src https raw githubusercontent com cvam0000 rtos master extra rtos png width 450 title p what rtos needs to be deterministic interrupt context switch preemption latency jitter faster linux mainline kernel was not designed as rtos distributions a href https www freertos org about rtos html freertos a freertos is a class of rtos that is designed to be small enough to run on a microcontroller although its use is not limited to microcontroller applications a href http blackberry qnx com en sdp7 qnx a qnx is real time rtos which support arm mips powerpc sh and x86 processor family a href https www windriver com products vxworks vxworks a vxworks is also real time rtos it support wide range of processor architectures like arm powerpc coldfire mips etc a href http www rtlinux org rtlinux a rtlinux is a hard realtime real time operating system rtos microkernel that runs the entire linux operating system as a fully preemptive process the hard real time property makes it possible to control robots data acquisition systems manufacturing plants and other time sensitive instruments and machines from rtlinux applications even with a similar name it is not related the real time linux project of the linux foundation a href http www chibios org dokuwiki doku php chibios rt a is designed for embedded applications on 8 16 and 32 bit microcontrollers size and execution efficiency are the main project goals 2 as reference the kernel size can range from a minimum of 1 2 kib up to a maximum of 5 5 kib with all the subsystems activated on a stm32 cortex m3 processor the kernel is capable of over 220 000 created terminated threads per second and is able to perform a context switch in 1 2 microseconds on an stm32 72 mhz similar metrics for all the supported platforms are included in the source distribution as test reports efficient and portable preemptive kernel best in class context switch performance many supported architectures and platforms static architecture everything is statically allocated at compile time dynamic extensions dynamic objects are supported by an optional layer built on top of the static core rich set of primitives threads virtual timers semaphores mutexes condition variables messages mailboxes event flags queues support for priority inheritance algorithm on mutexes hardware abstraction layer hal component supporting a variety of abstract device drivers port serial adc can ext gpt i2c icu mac mmc pwm rtc sdc spi uart usb usb cdc support for external components uip lwip fatfs extensive test suite with benchmarks support for c applications github https github com chibios projects 1 arduino uno atmega328p 8 bit c os freertos lib https github com feilipu arduino freertos library example 1 blink analogread example 2 ultrasonic sensor response at real time 2 raspberry pi 3 model b os rtlinux cross compile the rtlinux on any linux distro step 1 download the raspberrypi tool kernel sources https github com raspberrypi tools git git clone b rpi 3 18 9 rt5 https github com emlid linux rt rpi git cd linux rt rpi step 2 export the following variables to specify cross compilation export arch arm export cross compile tools arm bcm2708 gcc linaro arm linux gnueabihf raspbian x64 bin arm linux gnueabihf for raspberry pi 3 make bcm2709 rt defconfig compile the kernel make j5 install modules will result in lib folder with modules and firmware mkdir kernel rt install mod path kernel rt make modules install testing real time capabilites using cyclictest utility git clone git git kernel org pub scm linux kernel git clrkwllms rt tests git cd rt tests make all sudo cp cyclictest usr bin cd sudo cyclictest l1000000 m n a0 t1 p99 i400 h400 q rtos for single processor systems | os |
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tidybot | tidybot this code release accompanies the following project tidybot personalized robot assistance with large language models jimmy wu rika antonova adam kan marion lepert andy zeng shuran song jeannette bohg szymon rusinkiewicz thomas funkhouser autonomous robots auro special issue large language models in robotics 2023 ieee rsj international conference on intelligent robots and systems iros 2023 project page https tidybot cs princeton edu pdf https tidybot cs princeton edu paper pdf arxiv https arxiv org abs 2305 05658 video https youtu be bckdynx1kmq abstract for a robot to personalize physical assistance effectively it must learn user preferences that can be generally reapplied to future scenarios in this work we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away a key challenge is determining the proper place to put each object as people s preferences can vary greatly depending on personal taste or cultural background for instance one person may prefer storing shirts in the drawer while another may prefer them on the shelf we aim to build systems that can learn such preferences from just a handful of examples via prior interactions with a particular person we show that robots can combine language based planning and perception with the few shot summarization capabilities of large language models llms to infer generalized user preferences that are broadly applicable to future interactions this approach enables fast adaptation and achieves 91 2 accuracy on unseen objects in our benchmark dataset we also demonstrate our approach on a real world mobile manipulator called tidybot which successfully puts away 85 0 of objects in real world test scenarios https github com jimmyyhwu tidybot assets 6546428 dc1e87d9 f931 4741 916c 3dd11127b485 https github com jimmyyhwu tidybot assets 6546428 6b21b3c3 d360 4d58 89d6 6c1dfd57ac19 https github com jimmyyhwu tidybot assets 6546428 73f61e30 90ad 4f40 a5c8 1007adad897b https github com jimmyyhwu tidybot assets 6546428 643fe718 12b2 44ca afa7 f6ff2ddc304c https github com jimmyyhwu tidybot assets 6546428 afc749b6 0c3b 4b8e a0d2 ddd1ba0a6bf5 https github com jimmyyhwu tidybot assets 6546428 12e51dea 34a4 4c91 8423 34757bb485b7 overview here is an overview of how this codebase is organized server server server code for tidybot runs on gpu workstation robot robot robot code for tidybot runs on mobile base computer stl stl files for 3d printed parts benchmark benchmark code for the benchmark dataset setup we recommend using conda https docs conda io en latest miniconda html environments our setup tested on ubuntu 20 04 6 lts uses the following 3 environments 1 tidybot env on the server for general use 2 tidybot env on the robot for general use 3 vild env on the server for object detection only see the respective readmes inside the server server and robot robot directories for detailed setup instructions tidybot quickstart unless otherwise specified the tidybot conda env should always be used bash conda activate tidybot teleoperation mode we provide a teleoperation interface teleop py server teleop py to operate the robot using primitives such as pick place or toss first run this command to start the teleop interface on the server workstation where robot num is 1 2 or 3 depending on the robot to be controlled bash python teleop py robot num robot num on the robot mobile base computer make sure that the convenience stop and mobile base driver are both running then run this command to start the controller bash python controller py once the server and robot both show that they have successfully connected to each other use these controls to teleop the robot click on the overhead image to select waypoints press enter to execute selected waypoints on the robot press esc to clear selected waypoints or to stop the robot press 0 through 5 to change the selected primitive press q to quit if necessary use the convenience stop to kill the controller if necessary use the e stop to cut power to the robot the mobile base computer will stay on notes if keypresses are not registering make sure that the teleop interface is the active window the default primitive index 0 is movement only no arm to use the arm you will need to change the selected primitive to something else check the terminal output to see the list of all primitives as well as the currently selected primitive to generate paths with an occupancy map rather than manually clicking waypoints use the shortest path flag bash python teleop py robot num robot num shortest path this will load the receptacles specified in scenarios test yml server scenarios test yml as obstacles and build an occupancy map to avoid running into them for additional debugging visualization the debug flag can be used server bash python teleop py robot num robot num debug robot bash python controller py debug fully autonomous mode to operate the robot in fully autonomous mode we use the demo interface in demo py server demo py by default the demo will load the test scenario in scenarios test yml server scenarios test yml along with the corresponding llm summarized user preferences in preferences test yml server preferences test yml to start the demo on the server first start the object detector server with the vild conda env bash conda activate vild python object detector server py then in a separate terminal start the demo interface with the tidybot env bash python demo py robot num robot num on the robot make sure that the convenience stop and mobile base driver are both running then run this command to start the controller bash python controller py these are the controls used to run the demo press enter to start the robot press esc to stop the robot at any time press 0 to enter supervised mode the default mode in which the robot will wait for human approval via an enter keypress before executing every command press 1 to enter autonomous mode in which the robot will start executing commands whenever enter is pressed and stop moving whenever esc is pressed press q to quit if necessary use the convenience stop to kill the controller if necessary use the e stop to cut power to the robot the mobile base computer will stay on note if keypresses are not registering make sure that the demo interface is the active window to load a different scenario default is test use the scenario name argument bash python demo py robot num robot num scenario name scenario name for example to load scenario 08 and use robot 1 you can run bash python demo py robot num 1 scenario name scenario 08 for additional debugging visualization the debug flag can be used server bash python demo py robot num robot num debug robot bash python controller py debug troubleshooting mobile base accuracy the marker detection setup should output 2d robot pose estimates with centimeter level accuracy for instance our setup can reliably pick up small lego duplo blocks 32 mm x 32 mm from the floor inaccurate marker detection can be due to many reasons such as inaccurate camera alignment or suboptimal camera settings e g exposure and gain see get video cap in utils py server utils py also note that the mobile base motors should be calibrated motor cal txt for more accurate movement arm accuracy the 3 kinova arms are repeatable but have slightly different zero heading positions so they require some compensation to be consistent with each other see the arm dependent heading compensation in controller py robot controller py server ports if multiple people have been using the server you may run into this error oserror errno 98 address already in use to kill all processes using the occupied ports you can use the clear ports sh server clear ports sh script requires sudo bash clear ports sh for reference here are all of the ports used by this codebase 6000 camera server top camera 6001 camera server bottom camera 6002 marker detector server 6003 object detector server 6004 robot 1 controller server 6005 robot 2 controller server 6006 robot 3 controller server 6007 robot 1 control 6008 robot 2 control 6009 robot 3 control 6010 robot 1 camera 6011 robot 2 camera 6012 robot 3 camera camera errors the overhead cameras may occasionally output errors such as this warn 16 1367 080 global io opencv modules videoio src cap v4l cpp 1013 tryioctl videoio v4l2 dev v4l by id usb 046d logitech webcam c930e e4298f4e video index0 select timeout warn 16 2049 229 global io opencv modules videoio src cap v4l cpp 1013 tryioctl videoio v4l2 dev v4l by id usb 046d logitech webcam c930e 099a11ee video index0 select timeout corrupt jpeg data 36 extraneous bytes before marker 0xd9 corrupt jpeg data premature end of data segment typically these errors can be resolved by unplugging the camera and plugging it back in be sure to also check the quality and length of the usb extension cable as usb 2 0 does not support cable lengths longer than 5 meters citation if you find this work useful for your research please consider citing article wu2023tidybot title tidybot personalized robot assistance with large language models author wu jimmy and antonova rika and kan adam and lepert marion and zeng andy and song shuran and bohg jeannette and rusinkiewicz szymon and funkhouser thomas journal autonomous robots year 2023 | gpt-3 large-language-models mobile-manipulation robotics | ai |
simple-blockchain | simple blockchain my implementation of a blockchain in c i created for fun note that this was written before knowing more about distributed system concepts use at your own discretion follows some bitcoin design principles including a peer to peer network sha 256 to hash headers and blocks merkle trees and mining more on that below requirements uses c 14 openssl library simple web server and a json library https github com nlohmann json includes a command line interface that allows you to view blockchains at different indices and add new blocks you can do that 20 times until it automatically quits but you can change that control c to quit and unfortunately everything is stored in memory and is deleted when program quits peer to peer network at first i used websockets but a peer to peer system would require setting up a ws server and ws clients for each and every node so instead i make http requests to connect to nodes to the network for this to work we need to keep track of nodes in the network get the latest chains from every node to validate your chain get up to date when a new node is added to the network send out your chain to the network when a new block is added conflicts in different chains there can only be one explicit set of blocks in the chain at a given time if there are conflicts e g when the chains at different nodes have the same size but have different blocks the longest chain is chosen so if some other node sends in a new chain that s longer than yours your chain is replaced note this was a simple implementation and thus it replaces the entire chain except for the genesis block for future improvements each node should check the new chain with other nodes before it is added and entire nodes shouldn t be sent out i didn t have access to other computers in the same network so the nodes are connected through different ports inside your computer blockchain object private variables blockchain vector unique ptr block vector of smart pointers to block objects genesis block is created during intialization validating the integrity of blocks every time you want to add a block to the blockchain you will need to provide it int index string prevhash string hash string nonce vector string merkle index index of the block prevhash hash of the previous block nonce self explantory merkle vector holding in the data of the block it will then check whether you have the correct hash it rehashes the information given if you have 00 in front and whether your index is correct note this is the only way to add to the blockchain mining for i guess proof of stake i made it very simple because i didn t want to spend much processing power so it s honestly not proof of stake but i just added it in for fun use findhash to get hash and nonce first two characters of the hash must be 0 e g 003d9dc40cad6b414d45555e4b83045cfde74bcee6b09fb42536ca2500087fd9 works block object hash header index prevhash merkleroot data nonce private variables index data vector of strings previoushash blockhash nonce for a block to be immutable its properties are private and there are only methods that return them but not update them common functions getmerkleroot const vector string merkle gets merkle root based on elements of a vector findhash int index string prevhash vector string merkle mining part finds hash and returns a std pair of the hash found and nonce used to find it author tk2 illinois edu | blockchain |
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Software_Engineering_Project | this repository contains source code and documentation relevent to this software engineering project documentation holds all project documents backend server holds application source code project and tests frontend client holds front end ui source code | server |
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basicActiveObjectDemo | basic active object demo this project demonstrates a basic c module using an active object pattern this project was created to provide an example code base supporting the following post https covemountainsoftware com 2021 04 20 what is an active object the associated blog post details three elements required to implement an active object an external api or method to send asynchronous requests a concurrency safe queue to hold the requests a thread to receive and execute the requests in this example the c module hwlockctrlservice hlcs provides these three elements in this module we find a c based external api providing an abstraction for external users where primary requests are all async and push events onto the internal queue a private internal queue modeled after freertos a private internal thread modeled after freertos like the original source project the hlcs implements a simple flat state machine to process the events this earlier post provided the origin for this repository and the example active object behavior https covemountainsoftware com 2020 04 17 unit testing active objects and state machines platform this project should build and execute on any reasonable modern pc with the following toolchain supporting c11 and c 17 cmake version 3 16 or newer installation of cpputest version 3 8 this project was confirmed in the following environments macos catalina version 10 15 7 ubuntu 20 04 build targets hwlockctrlservicetests project demonstrating my approach to unit testing an event driven active object demopcapp this target is a trivial terminal demo app showing the target service in action for real references and inspiration 1 sutter herb prefer using active objects instead of naked threads dr dobbs june 2010 https www drdobbs com parallel prefer using active objects instead of n 225700095 2 grenning james test driven development for embedded c https amzn to 2ybanig 3 samek miro practical uml statecharts in c c event driven programming for embedded systems https amzn to 3exdhoz 4 just software solutions ltd blog implementing a thread safe queue using condition variables https www justsoftwaresolutions co uk threading implementing a thread safe queue using condition variables html 5 jason turner s c starter kit https github com lefticus cpp starter project 6 lars melchior s c starter kit https github com thelartians moderncppstarter 7 guy morand r4nd0m6uy blog on cmake and cpputest https r4nd0m6uy ch cmake and cpputest html 8 davis ford s github repo on cmake and cpputest https github com davisford cmake cpputest 9 earlier blog post on unit testing active objects https covemountainsoftware com 2020 04 17 unit testing active objects and state machines 10 the all c repo that originated this code https github com covemountainsoftware activeobjectunittestingdemo questions contact information is available here https covemountainsoftware com services consulting | os |
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qa-lora | qa lora this repository provides the pytorch implementation of qa lora quantization aware low rank adaptation of large language models https arxiv org pdf 2309 14717 pdf div align center img src image qalora png width 600 div qa lora is easily implemented with a few lines of code and it equips the original lora with two fold abilities i during fine tuning the llm s weights are quantized e g into int4 to reduce time and memory usage ii after fine tuning the llm and auxiliary weights are naturally integrated into a quantized model without loss of accuracy installation bash conda create n qalora python 3 8 conda activate qalora pip install torch torchvision torchaudio index url https download pytorch org whl cu117 git clone b peft integration https github com panqiwei autogptq git cd autogptq pip install triton cd git clone https github com timdettmers bitsandbytes git cd bitsandbytes cuda versions in 110 111 112 113 114 115 116 117 118 119 120 120 make argument in cuda110 cuda11x cuda12x if you do not know what cuda you have try looking at the output of python m bitsandbytes cuda version 117 make cuda11x python setup py install cd pip install git https github com huggingface transformers git pip install git https github com huggingface peft git pip install git https github com huggingface accelerate git pip install r requirements txt pip install protobuf 3 20 change the peft utils py in your own auto gptq path python path auto gptq utils peft utils py with the new one for the users of gptqlora https github com qwopqwop200 gptqlora you only need to change the peft utils py file quantization we use gptq https github com qwopqwop200 gptq for llama for quantization bits 4 group size 32 act order false if you change the group size you need to change the group size in peft utils py and merge py accordingly training bash python qalora py model path path the file structure of the model checkpoint is as follows config json llama7b 4bit 32g bin special tokens map json tokenizer config json generation config json quantize config json tokenizer model merge note that our trained lora modules can be perfectly merged into the quantized model we offer a simple merged script in this repo acknoledgements our code is based on qlora https github com artidoro qlora gptqlora https github com qwopqwop200 gptqlora auto gptq https github com panqiwei autogptq tree main | ai |
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