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klaytn | go report card https goreportcard com badge github com klaytn klaytn https goreportcard com report github com klaytn klaytn circleci https circleci com gh klaytn klaytn tree dev svg style svg https circleci com gh klaytn klaytn tree dev codecov https codecov io gh klaytn klaytn branch dev graph badge svg https codecov io gh klaytn klaytn godoc https godoc org github com klaytn klaytn status svg https pkg go dev github com klaytn klaytn gitter https badges gitter im klaytn lobby svg https gitter im klaytn lobby utm source badge utm medium badge utm campaign pr badge branch name will be changed we will change the master branch to main on dec 15 2022 after the branch policy change please check your local or forked repository settings klaytn official golang implementation of the klaytn protocol please visit klaytndocs https docs klaytn com for more details on klaytn design node operation guides and application development resources building from sources building the klaytn node binaries as well as utility tools such as kcn kpn ken kbn kscn kspn ksen kgen homi and abigen requires both a go version 1 14 1 or later and a c compiler you can install them using your favorite package manager once the dependencies are installed run make all or make kcn kpn ken kbn kscn kspn ksen kgen homi abigen executables after successful build executable binaries are installed at build bin command description kcn the cli client for klaytn consensus node run kcn help for command line flags kpn the cli client for klaytn proxy node run kpn help for command line flags ken the cli client for klaytn endpoint node which is the entry point into the klaytn network main test or private net it can be used by other processes as a gateway into the klaytn network via json rpc endpoints exposed on top of http websocket grpc and or ipc transports run ken help for command line flags kscn the cli client for klaytn servicechain consensus node run kscn help for command line flags kspn the cli client for klaytn servicechain proxy node run kspn help for command line flags ksen the cli client for klaytn servicechain endpoint node run ksen help for command line flags kbn the cli client for klaytn bootnode run kbn help for command line flags kgen the cli client for klaytn nodekey generation tool run kgen help for command line flags homi the cli client for klaytn helper tool to generate initialization files run homi help for command line flags abigen source code generator to convert klaytn contract definitions into easy to use compile time type safe go packages both kcn and ken are capable of running as a full node default or an archive node retaining all historical state running a core cell core cell cc is a set of one consensus node cn and one or more proxy nodes pns core cell plays a role of generating blocks in the klaytn network we recommend to visit the cc operation guide https docs klaytn com node core cell for the details of cc bootstrapping process running an endpoint node endpoint node en is an entry point from the outside of the network in order to interact with the klaytn network currently two official networks are available baobab testnet and cypress mainnet please visit the official en operation guide https docs klaytn com node endpoint node running a service chain node service chain node is a node for service chain which is an auxiliary blockchain independent from the main chain tailored for individual service requiring special node configurations customized security levels and scalability for high throughput services service chain expands and augments klaytn by providing a data integrity mechanism and supporting token transfers between different chains although the service chain feature is under development we provide the operation guide for testing purposes please visit the official document service chain operation guide https docs klaytn com node service chain furthermore for those who are interested in the klaytn service chain please check out klaytn scaling solutions https docs klaytn com klaytn scaling solutions license the klaytn library i e all code outside of the cmd directory is licensed under the gnu lesser general public license v3 0 https www gnu org licenses lgpl 3 0 en html also included in our repository in the copying lesser file the klaytn binaries i e all code inside of the cmd directory is licensed under the gnu general public license v3 0 https www gnu org licenses gpl 3 0 en html also included in our repository in the copying file contributing as an open source project klaytn always welcomes your contribution please read our contributing md contributing md for a walk through of the contribution process | blockchain-platform blockchain | blockchain |
Music-Store-DDD | music store ddd a music e shop app created to demo the domain driven design approach to software development | server |
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TP-Pago-Agil | pago agil project made in 2017 for a databases related course called data management of the information systems engineering career at the national technological university argentina pago agil is roughly translated to quick payment in english note this project was modified in 2019 to make the c better and more readable trabajo practico realizado en el a o 2017 para la materia gestion de datos de la carrera ingenieria en sistemas de informacion en la utn frba | server |
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multilingual-safety-for-LLMs | multilingual jailbreak challenges in large language models p align center style display flex flex direction row justify content center align items center a href https arxiv org abs 2310 06474 target blank style margin right 15px margin left 10px paper a a href https huggingface co datasets damo nlp sg multijail target blank style margin left 10px dataset a p this repo contains the data for our paper multilingual jailbreak challenges in large language models https arxiv org abs 2310 06474 annotation statistics we collected a total of 315 english unsafe prompts and annotated them into nine non english languages the languages were categorized based on resource availability as shown below high resource languages chinese zh italian it vietnamese vi medium resource languages arabic ar korean ko thai th low resource languages bengali bn swahili sw javanese jv introduction figure introduction jpg we identify the presence of multilingual jailbreak challenges within llms and propose to study them under two potential scenarios unintentional and intentional the unintentional scenario involves users querying llms using non english prompts and inadvertently bypassing the safety mechanisms while the intentional scenario concerns malicious users combining malicious instructions with multilingual prompts to attack llms deliberately dataset we carefully gather english harmful queries and manually translate them by native speakers into 9 non english languages ranging from high resource to low resource this leads us to the creation of the first multilingual jailbreak dataset called multijail the prompt in this dataset can directly serve for the unintentional scenario while we also simulate intentional scenario by combining the prompt with an english malicious instruction the language categories and their corresponding languages are as follows high resource chines zh italic it vietnamese vi medium resource arabic ar korean ko thai th low resource bengali bn swahili sw javanese jv the malicious instruction used in this work is aim https www jailbreakchat com prompt 4f37a029 9dff 4862 b323 c96a5504de5d result figure result jpg self defence to handle such a challenge in the multilingual context we propose a novel self defence framework that automatically generates multilingual training data for safety fine tuning figure algo jpg experimental results show that chatgpt fine tuned with such data can achieve a substantial reduction in unsafe content generation figure self defence result jpg ethics statement our research investigates the safety challenges of llms in multilingual settings we are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use misuse or harm resulting from the information in this paper is strongly discouraged to address the identified risks and vulnerabilities we commit to open sourcing the data used in our study this openness aims to facilitate vulnerability identification encourage discussions and foster collaborative efforts to enhance llm safety in multilingual contexts furthermore we have developed the self defense framework to address multilingual jailbreak challenges in llms this framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios overall our work not only highlights multilingual jailbreak challenges in llms but also paves the way for future research collaboration and innovation to enhance their safety citation misc deng2023multilingual title multilingual jailbreak challenges in large language models author yue deng and wenxuan zhang and sinno jialin pan and lidong bing year 2023 eprint 2310 06474 archiveprefix arxiv primaryclass cs cl | jailbreak llm safety multilingual | ai |
CMSIS-RTOS2_Validation | cmsis rtos2 validation this repository contains a test suite that validates cmsis rtos2 implementations it uses arm virtual hardware https www arm com virtual hardware to run a ci cd flow to verify correct operation of the real time operating systems rtos under test on various arm cortex m based processors arm virtual hardware provides simulation models software tooling and infrastructure that can be integrated into ci cd and mlops development flows the simulation models called arm virtual hardware targets are an implementation of a cortex m device sub systems and are designed for complex software verification and testing this allows simulation based test automation of various software workloads including unit tests integration tests and fault injection refer to the arm virtual hardware documentation https arm software github io avh main overview html index html for more information repository structure directory contents github workflows workflow yml files for running the test suite and for creating the documentation doxygen doxygen input files for creating the documentation include include files for test cases etc layer layers for creating the projects project an example project that shows unit testing script various shell scripts source test case source code test matrix currently the following tests are executed in the cmsis rv2 github workflows cmsis rv2 yml workflow rtos device compiler freertos cmsdk cm0plus vht ac6 gcc freertos cmsdk cm3 vht ac6 gcc freertos cmsdk cm4 fp vht ac6 gcc freertos cmsdk cm7 dp vht ac6 gcc freertos cmsdk cm7 sp vht ac6 gcc freertos iotkit cm23 vht ac6 gcc freertos iotkit cm33 fp vht ac6 gcc freertos sse 300 mps3 ac6 rtx5 cmsdk cm0plus vht ac6 gcc rtx5 cmsdk cm3 vht ac6 gcc rtx5 cmsdk cm4 fp vht ac6 gcc rtx5 cmsdk cm7 dp vht ac6 gcc rtx5 cmsdk cm7 sp vht ac6 gcc rtx5 iotkit cm23 vht ac6 gcc rtx5 iotkit cm33 fp vht ac6 gcc rtx5 sse 300 mps3 ac6 rtx5 see 310 mps3 ac6 gcc license license https img shields io badge license apache 2 0 blue svg https opensource org licenses apache 2 0 | os |
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Curso-JS-Avanzado-para-desarrolladores-Front-end_ed3 | shieldsio https img shields io github issues fictizia curso js avanzado para desarrolladores front end ed3 svg shieldsio https img shields io github forks fictizia curso js avanzado para desarrolladores front end ed3 svg shieldsio https img shields io github stars fictizia curso js avanzado para desarrolladores front end ed3 svg wideimg http fictizia com img github fictizia plan estudios github jpg curso de javascript avanzado para desarrolladores front end https fictizia com formacion curso javascript avanzado poo con js ecma6 patrones de dise o ajax avanzado html5 avanzado apis externas el curso de javascript avanzado para desarrolladores web est pensado para que sus alumnos ampl en sus habilidades con el desarrollo con javascript nativo y adquieran las capacidades necesarias para crear profesionalmente sitios web din micos a medida de las necesidades de cada proyecto el objetivo principal de este curso avanzado de js es que los alumnos sean capaces de integrarse en entornos de desarrollo modernos y eficientes incluyendo el uso de patrones de dise o control de versiones testing fundamentos de trabajo node js los alumnos tambi n aprender n todo lo necesario para crear aplicaciones que requieran de una base de datos gil y en tiempo real con firebase en el mundo de la web cada d a es m s necesario la integraci n de servicios de terceros utilizando apis durante el curso los alumnos aprender n a trabajar fluidamente con ajax y apis en el curso de javascript avanzado para desarrolladores web aprender n a mejorar la calidad del c digo gracias a t cnicas avanzadas de depuraci n documentaci n versionamiento sem ntico y gu as de estilo como metodolog a de trabajo durante el curso los alumnos desarrollar n como pr cticas troncales diversas aplicaciones web que servir n adem s de repositorio de todo lo aprendido el curso se gestiona ntegramente a trav s de github lo que permitir a los alumnos desarrollar las practicas en un entorno colaborativo ten en cuenta que este curso est dise ado para aquellos desarrolladores que ya tienen conocimientos de programaci n con javascript si no dispones de esos conocimientos o quieres asegurar que tu base en js es la adecuada te recomendamos que antes realices el curso de javascript para desarrolladores web http www fictizia com formacion curso javascript con este curso de javascript aprender s a desarrollar aplicaciones web complejas eficientes y sin necesidad de librer as ni frameworks conocer la programaci n orientada a objetos con prototipos en javascript trabajar fluidamente con firebase ser s capaz de trabajar fluidamente con proyectos complejos que requieran de ajax ser s capaz de integrar apis externas en tus proyectos ser s capaz de integrar las ltimas funcionalidades de html5 en tus proyectos tener la capacidad de integrar patrones de dise o en tus proyectos conocer c mo trabajar eficientemente con un control de versiones como git tener la capacidad de contribuir a proyectos de c digo abierto usando github trabajar con frameworks de testing utilizar node js para tareas de automatizaci n javascript avanzado para desarrolladores front end http www fictizia com formacion curso javascript avanzado sobre el curso horario martes y jueves de 19h a 22h fechas 07 09 2018 29 11 2018 festivos j 01 11 18 y j06 12 18 teor a y recursos teor a recursos descripci n clase 1 teoria clase1 md clase 1 recursos clase1 md intro al curso git github gitlab bitbucket clase 2 teoria clase2 md clase 2 recursos clase2 md chrome devtools reintro a javascript i clase 3 teoria clase3 md clase 3 recursos clase3 md reintro a javascript ii clase 4 teoria clase4 md clase 4 recursos clase4 md ecma6 es7 es8 es9 i clase 5 teoria clase5 md clase 5 recursos clase5 md ecma6 es7 es8 es9 ii clase 6 teoria clase6 md clase 6 recursos clase6 md paradigmas programaci n orientada a objetos clase 7 teoria clase7 md clase 7 recursos clase7 md bom y dom clase 8 teoria clase8 md clase 8 recursos clase8 md gesti n de eventos clase 9 teoria clase9 md clase 9 recursos clase9 md ajax clase 10 teoria clase10 md clase 10 recursos clase10 md web sockets clase 11 teoria clase11 md clase 11 recursos clase11 md firebase i intro realtime database clase 12 teoria clase12 md clase 12 recursos clase12 md firebase i autenticaci n y hosting clase 13 teoria clase13 md clase 13 recursos clase13 md html5 geo y mapas clase 14 teoria clase14 md clase 14 recursos clase14 md html5 localstorage y contenteditor clase 15 teoria clase15 md clase 15 recursos clase15 md html5 offline y webworkers clase 16 teoria clase16 md clase 16 recursos clase16 md patrones de js i antipatrones clase 17 teoria clase17 md clase 17 recursos clase17 md patrones de js ii metaprogramaci n y patrones clase 18 teoria clase18 md clase 18 recursos clase18 md arquitectura en js presentation patters mv mvc mvvm etc clase 19 teoria clase19 md clase 19 recursos clase19 md patrones de js iii algoritmia y estructuras de datos clase 20 teoria clase20 md clase 20 recursos clase20 md expresiones regulares regex clase 21 teoria clase21 md clase 21 recursos clase21 md nodejs funcionamiento ecosistema modularizaci n y librer as core clase 22 teoria clase22 md clase 22 recursos clase22 md npm yarn bower grunt gulp npm scripts clase 23 teoria clase23 md clase 23 recursos clase23 md testing quality tools clase 24 teoria clase24 md clase 24 repaso y fin de curso temario control de versiones git y github desarrollo en la nube con c9 io reintroducci n a javascript arrays objetos n meros cadenas funciones an nimas callbacks recursividad programaci n orientada a objetos con prototipos firebase nobackend base de datos nosql social login despliege dominando ajax json jsonp cors apis externas html5 api local storage selectors geolocalizaci n local storage offline drag drop websockets cliente web workers canvas indexeddb notification regexp expresiones regulares patrones mediador prototipo fa ade decorador namespace init time branching lazy definition revealing module pattern memoization m dulo singleton factory mvc testing metodolog as librer as introducci n a node js npm nvm single thread yeoman gulp bower http url ecma 6 constantes scoping arrow functions yield gesti n de par metros en funciones plantillas de texto mejoras en objetos asignaci n desestructurada nuevos m todos integrados for of generadores s mbolos map set clases m dulos buenas pr cticas estilos documentaci n herramientas github https github com cloud 9 https c9 io ulisesgascon jshint http www jshint com mastering markdown https guides github com features mastering markdown | front_end |
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Customer-Employee-Database-GUI | customer employee database gui final project for engineering database systems i created a database using database design concepts including referential and entity integrity i used sql scripts to create the tables and python to import the data from txt files i used html to create the gui front end and i used python to generate reports using sql embedded scripts in the files | server |
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design-system | swiss post design system swiss post design system splash screen https github com swisspost design system assets 1659006 c11afbac 8a71 4416 ae3c ec87f4e10412 the swiss post design system pattern library for a unified and accessible user experience across the web platform packages styles npm https img shields io npm v swisspost design system styles https www npmjs com package swisspost design system styles documentation https design system post ch changelog packages styles changelog md the styling package including theming for bootstrap https getbootstrap com and ng bootstrap https ng bootstrap github io home components bash npm install swisspost design system styles internet header npm https img shields io npm v swisspost internet header https www npmjs com package swisspost internet header documentation https next design system post ch path docs internet header getting started docs changelog packages internet header changelog md the header for client facing applications bash npm install swisspost internet header intranet header npm https img shields io npm v swisspost design system intranet header https www npmjs com package swisspost design system intranet header documentation https design system post ch post samples intranet layout changelog packages components angular changelog md the header angular component for internal usage bash npm install swisspost design system intranet header components npm https img shields io npm v swisspost design system components https www npmjs com package swisspost design system components documentation https next design system post ch changelog packages components changelog md a set of standard web components for easy integration with every framework or no framework at all bash npm install swisspost design system components design documentation experience hub https www experience hub ch document 2803 pattern usage documentation request access https www experience hub ch request access contribute contributor covenant https img shields io badge contributor 20covenant 2 1 4baaaa svg code of conduct md considering supporting the swiss post design system with your contribution whether you like to contribute new patterns fix a bug spotted a typo or have ideas for improvement we d love to hear from you read more about setting up the development environment and the different ways to get involved in our contribution guidelines contributing md for any questions regarding the pattern library you can reach out on the discussions page https github com swisspost design system discussions in order to keep our community open and inclusive we expect you to read and follow our code of conduct code of conduct md license software contained in this repository is published by the swiss post ltd under the apache 2 0 license license 2022 swiss post ltd | pattern-library design-system | os |
Agriculture-Chatbot-Using-NLP | agriculture chatbot using nlp agriculture chatbot using natural language processing nlp and artificial neural network ann | ai |
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100-Days-of-ML-Code-Chinese-Version | 100 avik jain https github com avik jain 100 days of ml code https github com machinelearning100 100 days of ml code faq https github com mleveryday 100 days of ml code blob master faq md 1 2 3 4 k k nn k k nn 7 svm svm 12 23 33 k k 43 54 1 https github com machinelearning100 100 days of ml code blob master code day 201 data preprocessing md p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 201 jpg p 2 https github com machinelearning100 100 days of ml code blob master code day 202 simple linear regression md p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 202 jpg p 3 https github com machinelearning100 100 days of ml code blob master code day 203 multiple linear regression md p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 203 png p 4 p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 204 jpg p 5 br github markdown 6 https github com machinelearning100 100 days of ml code blob master code day 206 logistic regression md k k nn 7 p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 207 jpg p 8 saishruthi swaminathan a href https towardsdatascience com logistic regression detailed overview 46c4da4303bc a br svm 9 svm k 10 svm knn k k nn 11 k k nn https github com machinelearning100 100 days of ml code blob master code day 2011 k nn md svm 12 p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 2012 jpg p svm 13 svm https github com machinelearning100 100 days of ml code blob master code day 2013 svm md svm 14 svm scikit learn scikit learn svc kernel trick python https github com machinelearning100 100 days of ml code blob master code day 2013 svm py jupyter notebook https github com machinelearning100 100 days of ml code blob master code day 2013 svm ipynb naive bayes classifier black box machine learning 15 a href https bloomberg github io foml home bloomberg a 16 scikit learn svm coursera 17 1 1 2 coursera 18 1 python yaser abu mostafa 19 yaser abu mostafa caltech cs156 1 2 20 1 21 beautiful soup 22 yaser abu mostafa caltech cs156 2 hoeffding 23 p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 2023 20 20chinese jpg p 24 bloomberg ml 3 25 https github com machinelearning100 100 days of ml code blob master code day 2025 decision tree md 26 youtube 3blue1brown https www youtube com channel ucyo jab esufrv4b17ajtaw 4 b https space bilibili com 88461692 channel detail cid 9450 27 4 b https space bilibili com 88461692 channel detail cid 9450 28 3 b https space bilibili com 88461692 channel detail cid 9450 29 12 14 b https space bilibili com 88461692 channel detail cid 9450 30 youtube 3 b https space bilibili com 88461692 channel detail cid 13407 31 2 b https space bilibili com 88461692 channel detail cid 13407 32 4 b https space bilibili com 88461692 channel detail cid 13407 33 p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 2033 png p 34 https github com machinelearning100 100 days of ml code blob master code day 2034 random forests md 1 35 youtube 3blue1brown b https space bilibili com 88461692 channel detail cid 26587 2 36 youtube 3blue1brown 2 169 b https space bilibili com 88461692 channel detail cid 26587 3 37 youtube 3blue1brown 3 b https space bilibili com 88461692 channel detail cid 26587 4 38 youtube 3blue1brown 3 b https space bilibili com 88461692 channel detail cid 26587 1 python tensorflow keras 39 https www youtube com watch v wq8bibpya2k t 19s index 2 list plqvvvaa0qudfhtox0ajmq6tvtgmbzbexn br notebook https github com machinelearning100 100 days of ml code blob master code day 2039 ipynb 2 python tensorflow keras 40 https www youtube com watch v wq8bibpya2k t 19s index 2 list plqvvvaa0qudfhtox0ajmq6tvtgmbzbexn br notebook https github com machinelearning100 100 days of ml code blob master code day 2040 ipynb 3 python tensorflow keras 41 https www youtube com watch v wq8bibpya2k t 19s index 2 list plqvvvaa0qudfhtox0ajmq6tvtgmbzbexn br notebook https github com machinelearning100 100 days of ml code blob master code day 2041 ipynb 4 python tensorflow keras 42 https www youtube com watch v wq8bibpya2k t 19s index 2 list plqvvvaa0qudfhtox0ajmq6tvtgmbzbexn br notebook https github com machinelearning100 100 days of ml code blob master code day 2042 ipynb k 43 http www avikjain me http shabal in visuals kmeans 6 html k p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 2043 jpg p k 44 numpy 45 jk vanderplas python python data science handbook jupyter notebooks https github com jakevdp pythondatasciencehandbook br pdf https github com machinelearning100 100 days of ml code blob master other 20docs python e6 95 b0 e6 8d ae e7 a7 91 e5 ad a6 e6 89 8b e5 86 8c zip br 2 numpy br br 2 numpy https github com jakevdp pythondatasciencehandbook blob master notebooks 02 00 introduction to numpy ipynb br 2 1 python https github com jakevdp pythondatasciencehandbook blob master notebooks 02 01 understanding data types ipynb br 2 2 numpy https github com jakevdp pythondatasciencehandbook blob master notebooks 02 02 the basics of numpy arrays ipynb br 2 3 numpy https github com jakevdp pythondatasciencehandbook blob master notebooks 02 03 computation on arrays ufuncs ipynb numpy 46 2 br br 2 4 https github com jakevdp pythondatasciencehandbook blob master notebooks 02 04 computation on arrays aggregates ipynb br 2 5 https github com jakevdp pythondatasciencehandbook blob master notebooks 02 05 computation on arrays broadcasting ipynb br 2 6 https github com jakevdp pythondatasciencehandbook blob master notebooks 02 06 boolean arrays and masks ipynb numpy 47 2 br br 2 7 https github com jakevdp pythondatasciencehandbook blob master notebooks 02 07 fancy indexing ipynb br 2 8 https github com jakevdp pythondatasciencehandbook blob master notebooks 02 08 sorting ipynb br 2 9 numpy https github com jakevdp pythondatasciencehandbook blob master notebooks 02 09 br structured data numpy ipynb pandas 48 3 pandas br pandas br br 3 pandas https github com jakevdp pythondatasciencehandbook blob master notebooks 03 00 introduction to pandas ipynb br 3 1 pandas https github com jakevdp pythondatasciencehandbook blob master notebooks 03 01 introducing pandas objects ipynb br 3 2 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 02 data indexing and selection ipynb br 3 3 pandas https github com jakevdp pythondatasciencehandbook blob master notebooks 03 03 operations in pandas ipynb br 3 4 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 04 missing values ipynb br 3 5 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 05 hierarchical indexing ipynb br 3 6 concat append https github com jakevdp pythondatasciencehandbook blob master notebooks 03 06 concat and append ipynb pandas 49 3 br br 3 7 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 07 merge and join ipynb br 3 8 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 08 aggregation and grouping ipynb br 3 9 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 09 pivot tables ipynb pandas 50 3 br br 3 10 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 10 working with strings ipynb br 3 11 https github com jakevdp pythondatasciencehandbook blob master notebooks 03 11 working with time series ipynb br 3 12 pandas eval query https github com jakevdp pythondatasciencehandbook blob master notebooks 03 12 performance eval and query ipynb matplotlib 51 4 matplotlib br br br 4 matplotlib https github com jakevdp pythondatasciencehandbook blob master notebooks 04 00 introduction to matplotlib ipynb br 4 1 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 01 simple line plots ipynb br 4 2 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 02 simple scatter plots ipynb br 4 3 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 03 errorbars ipynb br 4 4 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 04 density and contour plots ipynb matplotlib 52 4 matplotlib br br br 4 5 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 05 histograms and binnings ipynb br 4 6 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 06 customizing legends ipynb br 4 7 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 07 customizing colorbars ipynb br 4 8 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 08 multiple subplots ipynb br 4 9 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 09 text and annotation ipynb matplotlib 53 4 matplotlib br br 4 12 https github com jakevdp pythondatasciencehandbook blob master notebooks 04 12 three dimensional plotting ipynb 54 https github com machinelearning100 100 days of ml code blob master other 20docs e5 b1 82 e6 ac a1 e8 81 9a e7 b1 bb gif p align center img src https github com machinelearning100 100 days of ml code blob master info graphs day 2054 jpg p | ai |
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textc-csharp | textc textc is a natural language processing library that allows developers build text command based applications build status https ci appveyor com api projects status dd24q1u410h3nrct svg true https ci appveyor com project take textc csharp a href https www nuget org packages takenet textc rel nuhet nuget https img shields io nuget v takenet textc svg a how it works textc does the tokenization of text inputs by looking for matches in a collection of syntaxes which are grouped in a text processor the engine tries to parse the input using the defined token types in each syntax when there s more than one syntax match the processing engine choses the best match by using a scorer with the selected syntax an expression that holds the parsed tokens is generated being submitted to a command processor the most common command processor implementation binds expressions tokens to class delegates methods arguments thought a conversion of the token type to the language c type optionally the command processor can produce an output for instance the bound method can have a return value which is handled by an output processor context beside the text input the engine can also use the request context to fulfill a syntax token requirement the context is a dictionary of name value variables and its idea is to act like a natural conversation context when a person is in a natural language conversation at some moments he she can omit parts of the sentences because its implicit in the conversation context for instance john what brand is your car paul my car is a bmw john and what color paul it s yellow in the second question john didn t need to specify that he was talking about the car because it was implicit in the context and his real question was whats the color of your car so the car variable didn t needed to be in the conversation input this is the same idea of the textc context it is possible to add something in the context by marking a syntax token as contextual or during the command processing context using redis a href https www nuget org packages takenet textc extensions redis rel nuhet nuget https img shields io nuget v takenet textc svg a to use redis you must first install the extension package via nuget install package takenet textc extensions redis this package contains the class responsible for using redis instead of in memory of the application it is an implementation of irequestcontext interface csharp return new textc extensions redis redisrequestcontext redisendpoint redisdatabase redistimeout key csdl the command syntax definition language is a notation that allows the definition of syntaxes in a convenient way a csdl statement is composed by one or more token declarations each one specified in the following way name type initializer where name the name of the token to be extracted which must be unique in each statement this value can be used in the binding with method parameters or to store the value in the context optional type the type of the token to be extracted the library define some basic types like word and integer but the developer can define its own types mandatory initializer the initialization date for the token type which is used in specific ways accordingly to the type for instance in the word type it is used to define the set of words that can be parsed by the token type in some token types with complex initialization values this is presented in the json format optional for instance if we have a calculator that must accept commands like sum 1 and 2 where 1 and 2 are variable values the csdl for this syntax is command word sum num1 integer conjunction word and num2 integer we can simplify our syntax by omitting the name of tokens that we known that will not be used in the command execution will not be bind to a method parameter or stored in the context like some language constructs in this case the syntax will be word sum num1 integer word and num2 integer we also can define optional tokens in the syntax meaning that they can be present or not in the input context without changing the semantics of the text to notate that we must put a question mark character after the type definition word sum num1 integer word and num2 integer in this case the syntax can parse inputs like sum 1 2 but still sum 1 and 2 you can specify that a token value should be added to the context if the syntax is matched by using the plus character after the token name declaration which in this case is mandatory like this command word sum num1 integer word and num2 integer now if there s a match for this syntax the command variable will be added to the context with the token value sum and if next user input is something like 1 and 2 or even 1 2 this syntax will be matched this kind of token is called contextual by default the syntax parsing is done from left to right and a match can happen even if still is some text input to be consumed for instance our previous syntax will match sum 1 and 2 and 3 and 4 but only the first two numbers will be considered to avoid that we can add boundaries to the syntax surrounding it with the characters like this command word sum num1 integer word and num2 integer we can also change the initial parsing direction of the syntax by adding an anchor which is represented by the circumflex character in the start or the end of the syntax in the start is not required since by default the parsing is left to right to parse from the right to left we do command word sum num1 integer word and num2 integer and we can change the direction during the parsing by annoting the token type with a tilde character like this command word sum num1 integer word and num2 integer in this case the parsing starts from the right and if there s a match of the and word the parse of the input will continue from the left with the first non matched token command in this example this is useful when you have text token types which are greedy in the syntax if you need to match inputs like translate ol mundo to english you should have a syntax like this word translate text text word to language word english portuguese and you will have the value ol mundo assigned to the text token and the english value to the language token basic token types name description sample values decimal a culture invariant decimal number 1 123 0 1231 1 1 131 2 integer a 32 bit number 1 0 1 300 long a 64 bit number 1 0 1 292908889192986509 word a text word it consumes all the input until the next blank space character you can limit the expected words in the token template initialization multiple words enclosed within single quotes will be recognized as a single word dog cat banana text a text with multiple words note that this token type will consume all remaining input so it must be the last to be parsed in a syntax this is a sentence special token types name description sample values ldword a levenshtein distance https en wikipedia org wiki levenshtein distance word it parses any word with a default distance of 2 characters from the initialization values mispelled for misspelled dmword a double metaphone https en wikipedia org wiki metaphone word it matches if the generated metaphone code for the input is the same for any of the initialization values ekzampul for example regextext a text with an regex initializer note that this token type will consume all remaining input that matches the specified regex mycustom text regexlong a 64 bit number an regex initializer it allows more flexible parsing rules 4582379237123 regexword a text word with an regex initializer it allows more flexible parsing rules custom word pattern sword a soundex https en wikipedia org wiki soundex word it matches if the generated soundex code for the input is the same for any of the initialization values ekzampul for example samples calculator creating the text processor csharp fist define the calculator methods func int int task int sumfunc a b task fromresult a b func int int task int subtractfunc a b task fromresult a b func int int task int multiplyfunc a b task fromresult a b func int int task int dividefunc a b task fromresult a b after that the syntaxes for all operations using the csdl parser 1 sum a the default syntax for inputs like sum 1 and 2 or sum 3 4 var sumsyntax csdlparser parse operation word sum a integer word and b integer b the alternative syntax for inputs like 3 plus 4 var alternativesumsyntax csdlparser parse a integer word plus more b integer 2 subtract a the default syntax for inputs like subtract 2 from 3 var subtractsyntax csdlparser parse operation word subtract sub b integer word from a integer b the alternative syntax for inputs like 5 minus 3 var alternativesubtractsyntax csdlparser parse a integer word minus b integer 3 multiply a the default syntax for inputs like multiply 3 and 3 or multiply 5 2 var multiplysyntax csdlparser parse operation word multiply mul a integer word and by b integer b the alternative syntax for inputs like 6 times 2 var alternativemultiplysyntax csdlparser parse a integer word times b integer 4 divide a the default syntax for inputs like divide 3 by 3 or divide 10 2 var dividesyntax csdlparser parse operation word divide div a integer word by b integer b the alternative syntax for inputs like 6 by 2 var alternativedividesyntax csdlparser parse a integer word by b integer define a output processor that prints the command results to the console var outputprocessor new delegateoutputprocessor int o context console writeline result o now create the command processors to bind the methods to the syntaxes var sumcommandprocessor new delegatecommandprocessor sumfunc true outputprocessor sumsyntax alternativesumsyntax var subtractcommandprocessor new delegatecommandprocessor subtractfunc true outputprocessor subtractsyntax alternativesubtractsyntax var multiplycommandprocessor new delegatecommandprocessor multiplyfunc true outputprocessor multiplysyntax alternativemultiplysyntax var dividecommandprocessor new delegatecommandprocessor dividefunc true outputprocessor dividesyntax alternativedividesyntax finally create the text processor and register all command processors var textprocessor new textprocessor textprocessor commandprocessors add sumcommandprocessor textprocessor commandprocessors add subtractcommandprocessor textprocessor commandprocessors add multiplycommandprocessor textprocessor commandprocessors add dividecommandprocessor submitting the input csharp console write try await textprocessor processasync console readline context catch matchnotfoundexception console writeline there s no match for the specified input results sum 2 and 3 result 5 12 plus 3 result 15 subtract 30 from 90 result 60 77 minus 27 result 50 multiply 2 and 3 result 6 6 times 6 result 36 divide 30 by 5 result 6 100 by 10 result 10 sum two and three there s no match for the specified input calendar creating the text processor csharp 1 define the calendar syntaxes using some ldwords for input flexibility var addremindersyntax csdlparser parse word hey ok ldword calendar agenda word add new create command ldword remind reminder word me word to of message text word for when ldword today tomorrow someday var partialaddremindersyntax csdlparser parse word hey ok ldword calendar agenda word add new create command ldword remind reminder word for me when ldword today tomorrow someday var getreminderssyntax csdlparser parse when ldword today tomorrow someday ldword reminders 2 now the output processors var addreminderoutputprocessor new delegateoutputprocessor reminder reminder context console writeline reminder reminder message added successfully for reminder when var getremindersoutputprocessor new delegateoutputprocessor ienumerable reminder reminders context var remindersdictionary reminders groupby r r when todictionary r r key r r select reminder reminder message foreach var when in remindersdictionary keys console writeline reminders for when foreach var remindermessage in remindersdictionary when console writeline remindermessage console writeline 3 create a instance of the processor object to be shared by all processors var calendar new calendar 4 create the command processors var addremidercommandprocessor new reflectioncommandprocessor calendar nameof addreminderasync true addreminderoutputprocessor addremindersyntax var partialaddremidercommandprocessor new delegatecommandprocessor new func string task when console write what do you want to be reminded when return task fromresult 0 syntaxes partialaddremindersyntax var getremiderscommandprocessor new reflectioncommandprocessor calendar nameof getremindersasync true getremindersoutputprocessor getreminderssyntax 5 register the the processors var textprocessor new textprocessor textprocessor commandprocessors add addremidercommandprocessor textprocessor commandprocessors add partialaddremidercommandprocessor textprocessor commandprocessors add getremiderscommandprocessor 6 add some preprocessors to normalize the input text textprocessor textpreprocessors add new textnormalizerpreprocessor textprocessor textpreprocessors add new tolowercasepreprocessor return textprocessor the calendar class csharp public class calendar private readonly list reminder reminders public calendar reminders new list reminder public task reminder addreminderasync string message string when irequestcontext context var reminder new reminder message when reminders add reminder context clear return task fromresult reminder public task ienumerable reminder getremindersasync string when if string isnullorempty when return task fromresult ienumerable reminder reminders return task fromresult reminders where r r when equals when stringcomparison ordinalignorecase public class reminder public reminder string message string when message message when when someday public string message get public string when get submitting the input csharp console write try await textprocessor processasync console readline context catch matchnotfoundexception console writeline there s no match for the specified input results remind me to write some unit tests for the library reminder write some unit tests for the library added successfully for someday calendar remind to pay my bills today reminder pay my bills added successfully for today renind to learn to write in english tomorou reminder learn to write in english added successfully for tomorrow remind me to fix my keyboard reminder fix my keyboard added successfully for someday add reminder what do you want to be reminded to drink a coffee reminder drink a coffee added successfully for someday to drink a beer there s no match for the specified input remind me tomorrow what do you want to be reminded tomorrow to do the laundry reminder do the laundry added successfully for tomorrow reminders reminders for someday write some unit tests for the library fix my keyboard drink a coffee reminders for today pay my bills reminders for tomorrow learn to write in english do the laundry todai reminders reminders for today pay my bills todo x import code from private repository write new unit tests better documentation multiple culture support language specific token types verb subject conjunction language specific common syntaxes repository | ai |
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recipes | a href https substrate recipes substrate recipes a build status https img shields io endpoint svg url https 3a 2f 2factions badge atrox dev 2fsubstrate developer hub 2frecipes 2fbadge 3fref 3dmaster style flat markdown link check disable next line lines of code https tokei rs b1 github substrate developer hub recipes try on playground https img shields io badge playground recipes brightgreen logo parity 20substrate https playground substrate dev deploy recipes a hands on cookbook for aspiring blockchain chefs get started ready to roll up your sleeves and cook some blockchain read the book online at substrate recipes https substrate recipes repository structure there are five primary directories in this repository text source of the book https substrate recipes written in markdown this text describes the code in the other three directories pallets pallets for use in frame based runtimes runtimes runtimes for use in substrate nodes consensus consensus engines for use in substrate nodes nodes complete substrate nodes ready to run the book is built with mdbook https github com rust lang mdbook and deployed via github pages https pages github com building this book locally building the book requires mdbook ideally the same version that rust lang rust uses in this file rust mdbook to get it mdbook https github com rust lang nursery mdbook rust mdbook https github com rust lang rust blob master src tools rustbook cargo toml bash cargo install mdbook vers version num to build the book type bash mdbook build the output will be in the book subdirectory to check it out open up book index html in a web browser or to serve the book locally type bash mdbook serve the default address to view the book will be located at http localhost 3000 http localhost 3000 license the substrate recipes are gpl 3 0 licensed license it is open source and open for contributions contributing md using recipes in external projects the pallets and runtimes provided here are tested and ready to be used in other substrate based blockchains the big caveat is that you must use the same upstream substrate version throughout the project | substrate documentation rust blockchain | blockchain |
web-app-dev | github license https img shields io github license microsoft web dev for beginners svg https github com microsoft web dev for beginners blob master license github contributors https img shields io github contributors microsoft web dev for beginners svg https github com microsoft web dev for beginners graphs contributors github issues https img shields io github issues microsoft web dev for beginners svg https github com microsoft web dev for beginners issues github pull requests https img shields io github issues pr microsoft web dev for beginners svg https github com microsoft web dev for beginners pulls prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com github watchers https img shields io github watchers microsoft web dev for beginners svg style social label watch maxage 2592000 https github com microsoft web dev for beginners watchers github forks https img shields io github forks microsoft web dev for beginners svg style social label fork maxage 2592000 https github com microsoft web dev for beginners network github stars https img shields io github stars microsoft web dev for beginners svg style social label star maxage 2592000 https github com microsoft web dev for beginners stargazers open in visual studio code https img shields io static v1 logo visualstudiocode label message open 20in 20visual 20studio 20code labelcolor 2c2c32 color 007acc logocolor 007acc https open vscode dev microsoft web dev for beginners web development for beginners a curriculum learn the fundamentals of javascript css and html with our comprehensive 12 week course brought to you by microsoft cloud advocates each of the 24 lessons includes pre and post lesson quizzes detailed written instructions solutions assignments and much more with a project based approach to learning our curriculum is designed to help you develop practical skills through hands on building enhance your skills and optimize your knowledge retention with our effective project based pedagogy are you a student visit student hub page https docs microsoft com learn student hub wt mc id academic 77807 sagibbon where you will find beginner resources student packs and even ways to get a free certificate voucher this is the page you want to bookmark and check from time to time as we switch out content monthly getting started teachers we have included some suggestions for teachers md on how to use this curriculum we d love your feedback in our discussion forum https github com microsoft web dev for beginners discussions categories teacher corner students https aka ms student page wt mc id academic 77807 sagibbon to fully benefit from this curriculum we encourage you to fork the entire repository and engage in self study start with a pre lecture quiz and follow through with reading the lecture material and completing the various activities emphasize on comprehending the lessons rather than just copying the solution code however if needed the solution code can be found in the solutions folders within each project based lesson another great way to enhance your learning experience is to form a study group with your peers and work through the curriculum together to further your education we highly recommend exploring microsoft learn https learn microsoft com users wirelesslife collections p1ddcy5jwy0jkm wt mc id academic 77807 sagibbon for additional study materials pedagogy our curriculum is designed with two key pedagogical principles in mind project based learning and frequent quizzes the program teaches the fundamentals of javascript html and css as well as the latest tools and techniques used by today s web developers students will have the opportunity to develop hands on experience by building a typing game virtual terrarium eco friendly browser extension space invader style game and a banking app for businesses by the end of the series students will have gained a solid understanding of web development you can take the first few lessons in this curriculum as a learn path https docs microsoft com learn paths web development 101 wt mc id academic 77807 sagibbon on microsoft learn by ensuring that the content aligns with projects the process is made more engaging for students and retention of concepts will be augmented we also wrote several starter lessons in javascript basics to introduce concepts paired with a video from the beginners series to javascript https channel9 msdn com series beginners series to javascript wt mc id academic 77807 sagibbon collection of video tutorials some of whose authors contributed to this curriculum in addition a low stakes quiz before a class sets the intention of the student towards learning a topic while a second quiz after class ensures further retention this curriculum was designed to be flexible and fun and can be taken in whole or in part the projects start small and become increasingly complex by the end of the 12 week cycle while we have purposefully avoided introducing javascript frameworks to concentrate on the basic skills needed as a web developer before adopting a framework a good next step to completing this curriculum would be learning about node js via another collection of videos beginner series to node js https channel9 msdn com series beginners series to nodejs wt mc id academic 77807 sagibbon find our code of conduct code of conduct md contributing contributing md and translation translations md guidelines we welcome your constructive feedback each lesson includes optional sketchnote optional supplemental video pre lesson warmup quiz written lesson for project based lessons step by step guides on how to build the project knowledge checks a challenge supplemental reading assignment post lesson quiz a note about quizzes all quizzes are contained in this app https ashy river 0debb7803 1 azurestaticapps net for 48 total quizzes of three questions each they are linked from within the lessons but the quiz app can be run locally follow the instruction in the quiz app folder they are gradually being localized lessons project name concepts taught learning objectives linked lesson author 01 getting started introduction to programming and tools of the trade learn the basic underpinnings behind most programming languages and about software that helps professional developers do their jobs intro to programming languages and tools of the trade 1 getting started lessons 1 intro to programming languages readme md jasmine 02 getting started basics of github includes working with a team how to use github in your project how to collaborate with others on a code base intro to github 1 getting started lessons 2 github basics readme md floor 03 getting started accessibility learn the basics of web accessibility accessibility fundamentals 1 getting started lessons 3 accessibility readme md christopher 04 js basics javascript data types the basics of javascript data types data types 2 js basics 1 data types readme md jasmine 05 js basics functions and methods learn about functions and methods to manage an application s logic flow functions and methods 2 js basics 2 functions methods readme md jasmine and christopher 06 js basics making decisions with js learn how to create conditions in your code using decision making methods making decisions 2 js basics 3 making decisions readme md jasmine 07 js basics arrays and loops work with data using arrays and loops in javascript arrays and loops 2 js basics 4 arrays loops readme md jasmine 08 terrarium 3 terrarium solution readme md html in practice build the html to create an online terrarium focusing on building a layout introduction to html 3 terrarium 1 intro to html readme md jen 09 terrarium 3 terrarium solution readme md css in practice build the css to style the online terrarium focusing on the basics of css including making the page responsive introduction to css 3 terrarium 2 intro to css readme md jen 10 terrarium 3 terrarium solution readme md javascript closures dom manipulation build the javascript to make the terrarium function as a drag drop interface focusing on closures and dom manipulation javascript closures dom manipulation 3 terrarium 3 intro to dom and closures readme md jen 11 typing game 4 typing game solution readme md build a typing game learn how to use keyboard events to drive the logic of your javascript app event driven programming 4 typing game typing game readme md christopher 12 green browser extension 5 browser extension solution readme md working with browsers learn how browsers work their history and how to scaffold the first elements of a browser extension about browsers 5 browser extension 1 about browsers readme md jen 13 green browser extension 5 browser extension solution readme md building a form calling an api and storing variables in local storage build the javascript elements of your browser extension to call an api using variables stored in local storage apis forms and local storage 5 browser extension 2 forms browsers local storage readme md jen 14 green browser extension 5 browser extension solution readme md background processes in the browser web performance use the browser s background processes to manage the extension s icon learn about web performance and some optimizations to make background tasks and performance 5 browser extension 3 background tasks and performance readme md jen 15 space game 6 space game solution readme md more advanced game development with javascript learn about inheritance using both classes and composition and the pub sub pattern in preparation for building a game introduction to advanced game development 6 space game 1 introduction readme md chris 16 space game 6 space game solution readme md drawing to canvas learn about the canvas api used to draw elements to a screen drawing to canvas 6 space game 2 drawing to canvas readme md chris 17 space game 6 space game solution readme md moving elements around the screen discover how elements can gain motion using the cartesian coordinates and the canvas api moving elements around 6 space game 3 moving elements around readme md chris 18 space game 6 space game solution readme md collision detection make elements collide and react to each other using keypresses and provide a cooldown function to ensure performance of the game collision detection 6 space game 4 collision detection readme md chris 19 space game 6 space game solution readme md keeping score perform math calculations based on the game s status and performance keeping score 6 space game 5 keeping score readme md chris 20 space game 6 space game solution readme md ending and restarting the game learn about ending and restarting the game including cleaning up assets and resetting variable values the ending condition 6 space game 6 end condition readme md chris 21 banking app 7 bank project solution readme md html templates and routes in a web app learn how to create the scaffold of a multipage website s architecture using routing and html templates html templates and routes 7 bank project 1 template route readme md yohan 22 banking app 7 bank project solution readme md build a login and registration form learn about building forms and handling validation routines forms 7 bank project 2 forms readme md yohan 23 banking app 7 bank project solution readme md methods of fetching and using data how data flows in and out of your app how to fetch it store it and dispose of it data 7 bank project 3 data readme md yohan 24 banking app 7 bank project solution readme md concepts of state management learn how your app retains state and how to manage it programmatically state management 7 bank project 4 state management readme md yohan offline access you can run this documentation offline by using docsify https docsify js org fork this repo install docsify https docsify js org quickstart on your local machine and then in the root folder of this repo type docsify serve the website will be served on port 3000 on your localhost localhost 3000 pdf a pdf of all of the lessons can be found here https microsoft github io web dev for beginners pdf readme pdf other curricula our team produces other curricula check out ai for beginners https aka ms ai beginners data science for beginners https aka ms datascience beginners iot for beginners https aka ms iot beginners machine learning for beginners https aka ms ml beginners xr development for beginners https aka ms xr dev for beginners license this repository is licensed under the mit license see the license license file for more information | front_end |
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Natural-Language-Processing-NLP-Roadmap | natural language processing nlp helps computer to understand human language and also allows machines to communicate with us according to the wiki definition natural language processing nlp is an area of computer science and artificial intelligence concerned with the interactions between computers and human natural languages in particular how to program computers to fruitfully process large amounts of natural language data nlp applications 1 translating the languages 2 text processing in various languages 3 automatic text summarization 4 analyzing sentiments 5 speech recognition 6 named entity recognition 7 phrase extraction 8 tense identification 9 relationship extraction etc roadmap 1 stats and probability 2 text preprocessing 3 information extraction 4 feature extraction 5 part of speech tagging 6 named entity extraction 7 wordembedding 8 text similarity 9 semantic similarity 10 text clustering 11 text classification 12 sentiment 13 text summarization 14 chatbot 15 machine translation 16 text to speech 17 speech to text books 1 linear algebra by gilbert strang https math mit edu gs linearalgebra 2 information retrieval https nlp stanford edu ir book 3 mastering nlp with python http file allitebooks com 20160919 mastering 20natural 20language 20processing 20with 20python pdf 4 neural network http neuralnetworksanddeeplearning com 5 artificial intelligence a modern approach 4th edition 1 https github com pemagrg1 ai class2022 blob main book artificial intelligence a modern approach 4th edition 1 compressed pdf 6 alppaydin machinelearning 2010 https github com pemagrg1 ai class2022 blob main book alppaydin machinelearning 2010 pdf 7 artificial intelligence structures and strategies for complex problem solving sixth edition george f luger https github com pemagrg1 ai class2022 blob main book artificial 20intelligence 20structures 20and 20strategies 20for 20complex 20problem 20solving 20sixth 20edition 20 george 20f 20luger 20 z lib org pdf dataset 1 google nlp dataset https ai google tools datasets 2 uci repository https archive ics uci edu ml datasets php 3 nlp datasets https github com niderhoff nlp datasets course 1 intro to machine learning https www udacity com course intro to machine learning ud120 2 machine learning https www coursera org learn machine learning home welcome 3 neural network https courses analyticsvidhya com courses getting started with neural networks 4 deep learning https classroom udacity com courses ud730 2 neural network and deep learning https www coursera org learn neural networks deep learning home welcome 4 zero to deep learning https www udemy com zero to deep learning ranmid 39197 raneaid jbc0n5zkdzk ransiteid jbc0n5zkdzk ckp6 dy2hfoqwt5axbpqcq siteid jbc0n5zkdzk ckp6 dy2hfoqwt5axbpqcq lsnpubid jbc0n5zkdzk 5 courses in kaggle https www kaggle com learn roadmap pre requisites matrix multiplication https www mathsisfun com algebra matrix multiplying html set theory 1 stats and probability basic statistics in python probability https www dataquest io blog basic statistics in python probability basic sstatistics sin spython s sprobability 2 text preprocessing preprocessing using spacy https blog ekbana com nlp for beninners using spacy 6161cf48a229 preprocessing using nltk https becominghuman ai nlp for beginners using nltk f58ec22005cd preprocessing using stanza https pemagrg medium com nlp using stanza 3775c7e00f2a 3 information extraction information extraction in python https www analyticsvidhya com blog 2020 06 nlp project information extraction information retrieval https nlp stanford edu ir book 4 feature extraction feature selection tool for machine learning https towardsdatascience com a feature selection tool for machine learning in python b64dd23710f0 5 part of speech tagging nepali pos tagging https blog ekbana com nepali part of speech pos tagging 72eff56111c0 hindi pos tagging https blog ekbana com hindi part of speech pos tagging 5c3b8a6302b4 6 named entity extraction crf model https medium com data science in your pocket named entity recognition ner using conditional random fields in nlp 3660df22e95c 7 wordembedding one hot encoding https medium com zero equals false one hot encoding 129ccc293cda bag of words https www analyticsvidhya com blog 2021 08 a friendly guide to nlp bag of words with python example tfidf https medium com analytics vidhya magic of tf idf 202649d39c2f word2vec https www geeksforgeeks org python word embedding using word2vec glove https medium com analytics vidhya basics of using pre trained glove vectors in python d38905f356db 8 text similarity text similarity using bert https www analyticsvidhya com blog 2021 05 measuring text similarity using bert jaccard similarity https studymachinelearning com jaccard similarity text similarity metric in nlp text jaccard 20similarity 20is 20also 20known are 20exist 20over 20total 20words cosine similarity https studymachinelearning com cosine similarity text similarity metric 9 semantic similarity calculating the similarity between words and sentences using a lexical database and corpus statistics https arxiv org pdf 1802 05667 pdf 10 text clustering text clustering with tfidf https medium com mlearning ai text clustering with tf idf in python c94cd26a31e7 text clustering using kmeans https towardsdatascience com text clustering using k means ec19768aae48 11 text classification text classification using machine learning https blog ekbana com supervised text classification using machine learning b2466c63fb51 12 sentiment sentiment analysis https towardsdatascience com a beginners guide to sentiment analysis in python 95e354ea84f6 13 text summarization extractive text summary https blog ekbana com automatic text summarization 542b78163429 14 chatbot aiml https pemagrg medium com aiml tutorial a8802830f2bf source your stories page aiml chatbot https blog ekbana com the easiest way to create a chatbot using aiml ec09b12dd2e1 rasa https rasa com blog category tutorials 15 machine translation flores https github com facebookresearch flores mbart https huggingface co facebook mbart large 50 many to many mmt 16 text to speech taccotron https arxiv org abs 1703 10135 17 speech to text audio signal processing https blog ekbana com audio signal processing f7e86d415489 deepspeech https deepspeech readthedocs io en r0 9 others 1 web applications and api flask flask tutorial https www tutorialspoint com flask index htm build a web app using python s flask for beginners https pemagrg medium com build a web app using pythons flask for beginners f28315256893 fastapi https fastapi tiangolo com tutorial django https docs djangoproject com en 4 0 intro tutorial01 2 database mysql https www w3schools com mysql default asp postgres https www postgresqltutorial com mongodb https docs mongodb com manual tutorial | nlp natural-language-processing nlp-machine-learning nlp-roadmap text-processing text-classification text-mining 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Face-Mask-Detection | h1 align center face mask detection h1 div align center img src https github com vrushti24 face mask detection blob logo logo facemaskdetection ai 20 40 2051 06 25 20 cmyk gpu 20preview 20 2018 02 2021 2018 33 18 20 2 png width 200 height 200 h4 face mask detection system built with opencv keras tensorflow using deep learning and computer vision concepts in order to detect face masks in static images as well as in real time video streams h4 div nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp python https img shields io badge python v3 6 blue svg contributions welcome https img shields io badge contributions welcome brightgreen svg style flat https github com chandrikadeb7 face mask detection issues forks https img shields io github forks chandrikadeb7 face mask detection svg logo github https github com chandrikadeb7 face mask detection network members stargazers https img shields io github stars chandrikadeb7 face mask detection svg logo github https github com chandrikadeb7 face mask detection stargazers issues https img shields io github issues chandrikadeb7 face mask detection svg logo github https github com chandrikadeb7 face mask detection issues linkedin https img shields io badge linkedin black svg style flat square logo linkedin colorb 555 https www linkedin com in chandrika deb nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp live demo https github com chandrikadeb7 face mask detection blob master readme images demo gif point down support me here a href https www buymeacoffee com chandrikadeb7 target blank img src https www buymeacoffee com assets img custom images orange img png alt buy me a coffee style height 41px important width 174px important box shadow 0px 3px 2px 0px rgba 190 190 190 0 5 important webkit box shadow 0px 3px 2px 0px rgba 190 190 190 0 5 important a innocent motivation amid the ongoing covid 19 pandemic there are no efficient face mask detection applications which are now in high demand for transportation means densely populated areas residential districts large scale manufacturers and other enterprises to ensure safety the absence of large datasets of with mask images has made this task cumbersome and challenging ppt and project report sharing costs 1000 15 if interested contact me at chandrikadeb7 gmail com purchase on gumroad https gum co getfacemask hourglass project demo movie camera youtube demo link https youtu be wyww7gayyxw computer dev link https dev to chandrikadeb7 face mask detection my major project 3fj3 already deployed version https raw githubusercontent com vasantvohra trashnet master hr svg https face mask detection app herokuapp com p align center img src https github com chandrikadeb7 face mask detection blob master readme images screen 20shot 202020 05 14 20at 208 49 06 20pm png width 700 height 400 p warning techstack framework used opencv https opencv org caffe based face detector https caffe berkeleyvision org keras https keras io tensorflow https www tensorflow org mobilenetv2 https arxiv org abs 1801 04381 star features our face mask detector doesn t use any morphed masked images dataset and the model is accurate owing to the use of mobilenetv2 architecture it is computationally efficient thus making it easier to deploy the model to embedded systems raspberry pi google coral etc this system can therefore be used in real time applications which require face mask detection for safety purposes due to the outbreak of covid 19 this project can be integrated with embedded systems for application in airports railway stations offices schools and public places to ensure that public safety guidelines are followed file folder dataset the dataset used can be downloaded here click to download https github com chandrikadeb7 face mask detection tree master dataset this dataset consists of 4095 images belonging to two classes with mask 2165 images without mask 1930 images the images used were real images of faces wearing masks the images were collected from the following sources bing search api see python script https github com chandrikadeb7 face mask detection blob master search py kaggle datasets rmfd dataset see here https github com x zhangyang real world masked face dataset key prerequisites all the dependencies and required libraries are included in the file code requirements txt code see here https github com chandrikadeb7 face mask detection blob master requirements txt nbsp installation 1 clone the repo git clone https github com chandrikadeb7 face mask detection git 2 change your directory to the cloned repo cd face mask detection 3 create a python virtual environment named test and activate it virtualenv test source test bin activate 4 now run the following command in your terminal command prompt to install the libraries required pip3 install r requirements txt bulb working 1 open terminal go into the cloned project directory and type the following command python3 train mask detector py dataset dataset 2 to detect face masks in an image type the following command python3 detect mask image py image images pic1 jpeg 3 to detect face masks in real time video streams type the following command python3 detect mask video py key results our model gave 98 accuracy for face mask detection after training via code tensorflow gpu 2 5 0 code a href https colab research google com drive 1az0w2qahnm3rcj0qbtmc7c3fampcowq1 usp sharing img src https colab research google com assets colab badge svg alt open in colab a https github com chandrikadeb7 face mask detection blob master readme images screenshot 202020 06 01 20at 209 48 27 20pm png we got the following accuracy loss training curve plot https github com chandrikadeb7 face mask detection blob master plot png streamlit app face mask detector webapp using tensorflow streamlit command streamlit run app py images p align center img src readme images 1 png p p align center upload images p p align center img src readme images 2 png p p align center results p clap and it s done feel free to mail me for any doubts query email chandrikadeb7 gmail com internet of things device setup expected hardware raspberry pi 4 4gb with a case https www canakit com raspberry pi 4 4gb html 5mp ov5647 picamera from arducam https www arducam com docs cameras for raspberry pi native raspberry pi cameras 5mp ov5647 cameras getting started setup the raspberry pi case and operating system by following the getting started section on page 3 at documentation canakit raspberry pi quick start guide 4 0 pdf or https www canakit com media canakit raspberry pi quick start guide 4 0 pdf with noobs use the recommended operating system setup the picamera assemble the picamera case from arducam using documentation arducam case setup pdf or https www arducam com docs cameras for raspberry pi native raspberry pi cameras 5mp ov5647 cameras attach your picamera module to the raspberry pi and enable the camera https projects raspberrypi org en projects getting started with picamera 2 raspberry pi app installation execution run these commands after cloning the project commands time to completion sudo apt install y libatlas base dev liblapacke dev gfortran 1min sudo apt install y libhdf5 dev libhdf5 103 1min pip3 install r requirements txt 1 3 mins wget https raw githubusercontent com pinto0309 tensorflow bin master tensorflow 2 4 0 cp37 none linux armv7l download sh less than 10 secs tensorflow 2 4 0 cp37 none linux armv7l download sh less than 10 secs pip3 install tensorflow 2 4 0 cp37 none linux armv7l whl 1 3 mins trophy awards awarded runners up position in amdocs innovation india ice project fair https www amdocs com readme images nn jpeg raising hand cited by 1 https osf io preprints 3gph4 2 https link springer com chapter 10 1007 978 981 33 4673 4 49 3 https ieeexplore ieee org abstract document 9312083 4 https link springer com chapter 10 1007 978 981 33 4673 4 48 5 https www researchgate net profile akhyar ahmed publication 344173985 face mask detector links 5f58c00ea6fdcc9879d8e6f7 face mask detector pdf appreciation selected in devscript winter of code https devscript tech woc img src readme images devscript jpeg height 300 width 300 selected in script winter of code https swoc tech project html img src readme images winter jpeg height 300 width 300 seleted in student code in https scodein tech img src readme images sci jpeg height 300 width 300 1 credits https www pyimagesearch com https www pyimagesearch com https www tensorflow org tutorials images transfer learning https www tensorflow org tutorials images transfer learning handshake contribution please read the contribution guidelines here https github com chandrikadeb7 face mask detection blob master contributing md feel free to file a new issue with a respective title and description on the the face mask detection https github com chandrikadeb7 face mask detection issues repository if you already found a solution to your problem i would love to review your pull request handshake our contributors a href https github com chandrikadeb7 face mask detection graphs contributors img src https contributors img web app image repo chandrikadeb7 face mask detection a eyes code of conduct you can find our code of conduct here code of conduct md heart owner made with heart nbsp by chandrika deb https github com chandrikadeb7 eyes license mit chandrika deb https github com chandrikadeb7 face mask detection blob master license | python python-3 python3 keras keras-tensorflow deep-learning computer-vision face-mask-detection covid-19 ssd-mobilenet mobilenetv2 bing-search mask-detection facemaskdetect facemask-detection mask-detection-system face-mask machine-learning caffe hacktoberfest | ai |
1-x-web | web 1 x web download zip git clone https github com lydever web 1 x git 5 https user images githubusercontent com 65069676 138794485 0da23cd0 d855 4274 9ddf 67e74137f5fc png https user images githubusercontent com 65069676 138795044 094a405b a7f9 4ffa 9857 7a981e529e89 png 2020 2021 1 x 1 x web php https blog csdn net weixin 43853746 article details 108928068 1 x web jquery https blog csdn net weixin 43853746 article details 108916725 1 x web mysql https blog csdn net weixin 43853746 article details 109585844 1 x web web 12 https blog csdn net weixin 43853746 article details 109686767 1 x web 2020 12 web 1 https blog csdn net weixin 43853746 article details 109991848 1 x web 2020 12 web 1 https blog csdn net weixin 43853746 article details 109992935 1 x web 2020 12 web 2 https blog csdn net weixin 43853746 article details 109895005 1 x web 2020 12 web 2 https blog csdn net weixin 43853746 article details 109895208 1 x web 2020 12 web 3 https blog csdn net weixin 43853746 article details 109754127 1 x web 2020 12 web 3 https blog csdn net weixin 43853746 article details 109754600 1 x web 2020 12 web 4 https blog csdn net weixin 43853746 article details 109752494 1 x web 2020 12 web 4 https blog csdn net weixin 43853746 article details 109752094 1 x web 2020 12 web 5 https blog csdn net weixin 43853746 article details 109753433 1 x web 2020 12 web 5 https blog csdn net weixin 43853746 article details 109752995 | php js bootstrap4 mysql laravel css3 h5 jquery | front_end |
CV-CUDA | cv cuda license https img shields io badge license apache 2 0 yellogreen svg https opensource org licenses apache 2 0 version https img shields io badge version v0 4 0 beta blue platform https img shields io badge platform linux 64 7c win 64 wsl2 gray cuda https img shields io badge cuda v11 7 2376b900 logo nvidia https developer nvidia com cuda toolkit archive gcc https img shields io badge gcc v11 0 yellow https gcc gnu org gcc 11 changes html python https img shields io badge python v3 7 7c v3 8 7c v3 10 blue logo python https www python org cmake https img shields io badge cmake v3 22 23008fba logo cmake https cmake org cv cuda is an open source project that enables building efficient cloud scale artificial intelligence ai imaging and computer vision cv applications it uses graphics processing unit gpu acceleration to help developers build highly efficient pre and post processing pipelines cv cuda originated as a collaborative effort between nvidia nvidia develop and bytedance bytedance refer to our developer guide developer guide md for more information on the operators available as of release v0 4 0 beta getting started to get a local copy up and running follow these steps pre requisites linux distro ubuntu x86 64 20 04 wsl2 with ubuntu 20 04 tested with 20 04 nvidia driver linux driver version 520 56 06 or higher cuda toolkit version 11 7 or above 12 0 is not yet tested gcc 11 0 python 3 7 cmake 3 22 installation the following steps describe how to install cv cuda from pre built install packages choose the installation method that meets your environment needs tar file installation shell tar xvf nvcv lib 0 4 0 cuda11 x86 64 linux tar xz tar xvf nvcv dev 0 4 0 cuda11 x86 64 linux tar xz deb file installation shell sudo apt get install y nvcv lib 0 4 0 cuda11 x86 64 linux deb nvcv dev 0 4 0 cuda11 x86 64 linux deb python whl file installation shell pip install nvcv python 0 4 0 cp38 cp38 linux x86 64 whl build from source follow these instruction to build cv cuda from source 1 set up your local cv cuda repository 1 install prerequisites needed to setup up the repository on ubuntu 22 04 install the following packages git lfs to retrieve binary files from remote repository shell sudo apt get install y git git lfs 2 after cloning the repository assuming it was cloned in cvcuda it needs to be properly configured by running the init repo sh script only once shell cd cvcuda init repo sh 1 build cv cuda 1 install the dependencies required for building cv cuda on ubuntu 22 04 install the following packages g 11 compiler to be used cmake ninja build optional manage build rules python3 dev for python bindings libssl dev needed by the testsuite md5 hashing utilities shell sudo apt get install y g 11 cmake ninja build python3 dev libssl dev for cuda toolkit any version of the 11 x series should work cv cuda was tested with 11 7 thus it should be preferred shell sudo apt get install y cuda minimal build 11 7 2 build the project shell ci build sh this will compile a x86 release build of cv cuda inside build rel directory the library is in build rel lib docs in build rel docs and executables tests etc are in build rel bin the script accepts some parameters to control the creation of the build tree shell ci build sh release debug output build tree path by default it builds for release if output build tree path isn t specified it ll be build rel for release builds and build deb for debug 1 build documentation 1 install the dependencies required for building the documentation on ubuntu 22 04 install the following packages doxygen parse header files for reference documentation python3 python3 pip to install some python packages needed sphinx breathe exhale recommonmark graphiviz to render the documentation sphinx rtd theme documenation theme used shell sudo apt get install y doxygen graphviz python3 python3 pip sudo python3 m pip install sphinx 4 5 0 breathe exhale recommonmark graphviz sphinx rtd theme 2 build the documentation shell ci build docs sh build folder example ci build docs sh build docs 1 build and run samples 1 for instructions on how to build samples from source and run them see the samples samples readme md documentation 1 run tests 1 install the dependencies required for running the tests on ubuntu 22 04 install the following packages python3 python3 pip to run python bindings tests torch dependencies needed by python bindings tests shell sudo apt get install y python3 python3 pip sudo python3 m pip install pytest torch 2 run the tests the tests are in buildtree bin you can run the script below to run all tests at once here s an example when build tree is created in build rel shell build rel bin run tests sh 1 package installers installers can be generated using the following cpack command once you have successfully built the project shell cd build rel cpack this will generate in the build directory both debian installers and tarballs tar xz needed for integration in other distros for a fine grained choice of what installers to generate the full syntax is shell cpack g deb txz deb for debian packages txz for tar xz tarballs tools 1 cv cuda make operator tool this tool will create an noop operator python bindings and tests this tool is located in tools mkop to run it navigate to the directory and execute the command mkop sh operatorname where operatorname is the desired name of the operator contributing cv cuda is an open source project as part of the open source community we are committed to the cycle of learning improving and updating that makes this community thrive however as of release v0 4 0 beta cv cuda is not yet ready for external contributions to understand the process for contributing the cv cuda see our contributing contributing md page to understand our committment to the open source community and providing an environment that both supports and respects the efforts of all contributors please read our code of conduct code of conduct md license cv cuda operates under the apache 2 0 license md license security cv cuda as a nvidia program is committed to secure development practices please read our security security md page to learn more acknowledgements cv cuda is developed jointly by nvidia and bytedance nvidia develop https developer nvidia com bytedance https www bytedance com | gpu cloud computer-vision cpp cuda image-processing machine-learning python cv-cuda bytedance nvidia | ai |
moon_flutter | moon design system version https img shields io pub v moon design svg https pub dev packages moon design build https github com coingaming moon flutter actions workflows analyze and test yml badge svg conventional commits https img shields io badge conventional 20commits 1 0 0 23fe5196 logo conventionalcommits logocolor white https conventionalcommits org img width 644 alt moon design system src https user images githubusercontent com 232199 133601344 e63bd62f dd0f 47a1 9d1e b5cb065e5a90 png note this project uses release please https github com googleapis release please and conventional commits https www conventionalcommits org en v1 0 0 spec please follow the conventions or consider using commitizen https github com commitizen cz cli to write commit messages resources playground https flutter moon io applying theming and overrides declare tokens variable and optionally override values dart final lighttokens moontokens light copywith colors mooncolors light copywith piccolo colors blue textprimary colors amber typography moontypography typography copywith heading moontypography typography heading apply fontfamily dmsans final lighttheme themedata light copywith extensions themeextension dynamic moontheme tokens lighttokens or if needed override widget theming dart final lighttheme themedata light copywith extensions themeextension dynamic moontheme tokens lighttokens copywith accordiontheme moonaccordiontheme tokens lighttokens copywith colors moonaccordiontheme tokens lighttokens colors copywith backgroundcolor colors green apply the declared theme dart return materialapp title mds example theme lighttheme home const homepage | design-system flutter | os |
Sigwo-explorer-mirror | this explorer is forked from luke s explorer https github com iquidus explorer with a bit of flavoring from https github com masterhash us explorer which seems to be gone now cleaning up readme md history can be found in the first upload commit iquidus explorer 1 6 1 an open source block explorer written in node js requires node js 0 10 28 mongodb 2 6 x coind create database enter mongodb cli mongo create databse use explorerdb create user with read write access db createuser user iquidus pwd 3xp 0rer roles readwrite note if you re using mongo shell 2 4 x use the following to create your user db adduser user username pwd password roles readwrite get the source git clone https github com sigwotechnologies explorer install node modules cd explorer npm install production configure cp settings json template settings json make required changes in settings json start explorer npm start note mongod must be running to start the explorer as of version 1 4 0 the explorer defaults to cluster mode forking an instance of its process to each cpu core this results in increased performance and stability load balancing gets automatically taken care of and any instances that for some reason die will be restarted automatically for testing development or if you just wish to a single instance can be launched with node stack size 10000 bin instance to stop the cluster you can use npm stop syncing databases with the blockchain sync js located in scripts is used for updating the local databases this script must be called from the explorers root directory usage node scripts sync js database mode database required index mode main index coin info stats transactions addresses market market data summaries orderbooks trade history chartdata mode required for index database only update updates index from last sync to current block check checks index for and adds any missing transactions addresses reindex clears index then resyncs from genesis to current block notes current block is the latest created block when script is executed the market database only supports defaults to reindex mode if check mode finds missing data ignoring new data since last sync index timeout in settings json is set too low it is recommended to have this script launched via a cronjob at 1 min intervals crontab example crontab update index every minute and market data every 2 minutes 1 cd path to explorer usr bin nodejs scripts sync js index update dev null 2 1 2 cd path to explorer usr bin nodejs scripts sync js market dev null 2 1 5 cd path to explorer usr bin nodejs scripts peers js dev null 2 1 wallet iquidus explorer is intended to be generic so it can be used with any wallet following the usual standards the wallet must be running with atleast the following flags daemon txindex donate left luke s addresses please contribute btc 168hdka3fkccptkxnx8hbrsxnubvk4udji jbs jzp9893fmmrm1681bdujbu7c6w11kyey7d no longer valid use btc address known issues script is already running if you receive this message when launching the sync script either a a sync is currently in progress or b a previous sync was killed before it completed if you are certian a sync is not in progress remove the index pid from the tmp folder in the explorer root directory rm tmp index pid exceeding stack size rangeerror maximum call stack size exceeded nodes default stack size may be too small to index addresses with many tx s if you experience the above error while running sync js the stack size needs to be increased to determine the default setting run node v8 options grep b0 a1 stack size to run sync js with a larger stack size launch with node stack size size scripts sync js index update where size is an integer higher than the default note size will depend on which blockchain you are using you may need to play around a bit to find an optimal setting license copyright c 2015 iquidus technology copyright c 2015 luke williams all rights reserved redistribution and use in source and binary forms with or without modification are permitted provided that the following conditions are met redistributions of source code must retain the above copyright notice this list of conditions and the following disclaimer redistributions in binary form must reproduce the above copyright notice this list of conditions and the following disclaimer in the documentation and or other materials provided with the distribution neither the name of iquidus technology nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission this software is provided by the copyright holders and contributors as is and any express or implied warranties including but not limited to the implied warranties of merchantability and fitness for a particular purpose are disclaimed in no event shall the copyright holder or contributors be liable for any direct indirect incidental special exemplary or consequential damages including but not limited to procurement of substitute goods or services loss of use data or profits or business interruption however caused and on any theory of liability whether in contract strict liability or tort including negligence or otherwise arising in any way out of the use of this software even if advised of the possibility of such damage | blockchain |
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My-Ecart | my ecart my ecart is a full functionality e commerce website designed using bootstrap amp django it has tracking payment integration blog app etc bootstrap is used for frontend development and django is used for backend development note 1 while running project you may get no crypto module error even after installing pycrptodome to resolve this issue change installated crypto module folder name from crypto to crypto 2 for payment gateway use proper paytm gateway merchant key otherwise you will aes key length error | server |
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neoml | neoml neoml docs images neoml logo png desktop build status https img shields io azure devops build abbyyopensource 401f7fe0 92d9 411d 9839 60d3455fa1c0 2 master label desktop 20build python build https img shields io azure devops build abbyyopensource 401f7fe0 92d9 411d 9839 60d3455fa1c0 14 master label python 20build ios build https img shields io azure devops build abbyyopensource 401f7fe0 92d9 411d 9839 60d3455fa1c0 15 master label ios 20build android build https img shields io azure devops build abbyyopensource 401f7fe0 92d9 411d 9839 60d3455fa1c0 15 master label android 20build documentation status https readthedocs org projects neoml badge version latest https neoml readthedocs io en latest badge latest neoml is an end to end machine learning framework that allows you to build train and deploy ml models this framework is used by abbyy engineers for computer vision and natural language processing tasks including image preprocessing classification document layout analysis ocr and data extraction from structured and unstructured documents key features neural networks with support for over 100 layer types traditional machine learning 20 algorithms classification regression clustering etc cpu and gpu support fast inference onnx support languages python c java objective c cross platform the same code can be run on windows linux macos ios and android contents toc build and install build and install supported platforms supported platforms third party third party build fully functional c version build fully functional c version build inference versions for java and objective c build inference versions for java and objective c getting started getting started api description api description basic principles basic principles platform independence platform independence math engines independence math engines independence multi threading support multi threading support onnx support onnx support serialization format serialization format gpu support gpu support fineobj fineobj c interface c interface algorithms library neoml algorithms library neoml neomathengine neomathengine python module python module java interface java interface objective c interface objective c interface license license toc build and install supported platforms the full library version has been tested on the platforms target os compiler architecture windows 7 cpu and gpu msvc 2019 x86 x86 64 ubuntu 14 cpu gcc 5 4 x86 64 macos 10 11 cpu apple clang 12 arm64 x86 64 ios 11 cpu gpu apple clang 12 arm64 v8a x86 64 android 5 0 cpu android 7 0 gpu clang 7 armeabi v7a arm64 v8a x86 x86 64 the inference java and objective c library versions have been tested on the platforms target os compiler architecture ios 11 cpu gpu apple clang 12 arm64 v8a x86 64 android 5 0 cpu android 7 0 gpu clang 7 armeabi v7a arm64 v8a x86 x86 64 third party the library is built with cmake https cmake org download recommended versions 3 18 and later for best cpu performance on windows linux and macos we use intel mkl https software intel com en us mkl when processing on a gpu you can optionally use cuda https developer nvidia com cuda downloads version 11 2 upd 1 on windows or linux and vulkan https vulkan lunarg com sdk home version 1 1 130 and later on windows linux or android we also use google test https github com google googletest for testing and google protocol buffers https developers google com protocol buffers for working with onnx model format we use very convinous generator of jit code xbyak https github com herumi xbyak for speeding up some convolutions on x86 64 processors build fully functional c version see here neoml docs en installation cpp md for instructions on building the c library version for different platforms build inference versions for java and objective c see here neoml docs en installation inference md for instructions on building the java and objective c versions that would only run the trained neural networks getting started several tutorials with sample code will help you start working with the library train and use a simple network neoml docs en tutorial simplenet md classification with gradient boosting neoml docs en tutorial news20classification md data clustering with k means algorithm neoml docs en tutorial irisclustering md api description basic principles the library was developed with these principles in mind platform independence the user interface is completely separated from the low level calculations implemented by a math engine the only thing you have to do is to specify at the start the type of the math engine that will be used for calculations you can also choose to select the math engine automatically based on the device configuration detected the rest of your machine learning code will be the same regardless of the math engine you choose math engines independence each network works with one math engine instance and all its layers should have been created with the same math engine if you have chosen a gpu math engine it will perform all calculations this means you may not choose to use a cpu for light calculations like adding vectors and a gpu for heavy calculations like multiplying matrices we have introduced this restriction to avoid unnecessary synchronizations and data exchange between devices multi threading support the math engine interface neoml docs en api nn mathengine md is thread safe the same instance may be used in different networks and different threads note that this may entail some synchronization overhead however the neural network implementation neoml docs en api nn dnn md is not thread safe the network may run only in one thread onnx support neoml library also works with the models created by other frameworks as long as they support the onnx https onnx ai format see the description of import api neoml docs en onnx md however you cannot export a neoml trained model into onnx format serialization format the library uses its own binary format implemented by carchive carchivefile to save and load the trained models gpu support processing on gpu often helps significantly improve performance of mathematical operations the neoml library uses gpu both for training and running the models this is an optional setting and depends on the hardware and software capabilities of your system to work on gpu the library requires windows nvidia gpu card with cuda 11 2 upd 1 support ios apple gpu a7 android devices with vulkan 1 0 support linux macos no support for gpu processing as yet fineobj the neoml library originates in abbyy internal infrastructure for various reasons abbyy uses a cross platform framework called fineobj because of this the open library version uses some of this framework primitives see the common classes description neoml docs en api common readme md c interface neoml contains two c libraries algorithms library neoml the library provides c objects that implement various high level algorithms it consists of several parts neural networks neoml docs en api nn readme md classification and regression algorithms neoml docs en api classificationandregression readme md clustering algorithms neoml docs en api clustering readme md auxiliary algorithms neoml docs en api algorithms readme md neomathengine the math engine used for calculations is a separate module that implements the low level mathematical functions used in the algorithms library the user can also call these functions but usually never needs to this module has different implementations for different platforms in particular there is an implementation that uses a gpu for calculations the math engine is also a set of c interfaces described here neoml docs en api nn mathengine md python module see the extensive documentation of the python module on readthedocs io https neoml readthedocs io en latest index html java interface to work with the inference version of the library in java and kotlin we provide a java interface neoml docs en wrappers java md objective c interface to work with the inference version of the library in swift and objective c we provide an objective c interface neoml docs en wrappers objectivec md license copyright 2016 2020 abbyy production llc licensed under the apache license version 2 0 see the license file license | ml machine-learning deep-learning cpp onnx neural-network | ai |
conference_call_for_paper | 2021 2022 international conferences in artificial intelligence machine learning computer vision data mining natural language processing and robotics conference page https jackietseng github io conference call for paper conferences html conference page with ccf https jackietseng github io conference call for paper conferences with ccf html china computer federation recommended ranking for chinese student only todo list auto crawler add item abstract ddl notification due x add function sort by submission deadline p s h index data comes from google scholar https scholar google com citations view op top venues hl en ddls are denoted by tbd if they are not finalized and will be corrected once the conference updates the information these websites is maintained by jackie tseng tsinghua computer vision and intelligent learning lab | ai |
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Udacity_Front_End_Developer | udacity front end developer front end web developer nanodegree on udacity | front_end |
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ci-experiments | ci experiments coverity scan build status coverity scan https scan coverity com projects 13021 badge svg flat 1 https scan coverity com projects zebastian ci experiments linux osx build status https travis ci org zebastian ci experiments svg branch master https travis ci org zebastian ci experiments windows build status https ci appveyor com api projects status cb0woxak6orvynsl svg true https ci appveyor com project zebastian ci experiments analyse verschiedener cloud testing anbieter im rahmen der seminararbeit des fachs software engineering projekt das eigentliche projekt ist eine ausf hrbare dummy datei in src main c diese wird durch die cmake build konfiguration in cmakelists txt beschrieben testing provider travis in der datei travis yml findet sich die konfiguration des projekts zum bauen auf osx und linux appveyor in der datei appveyor yml findet sich die konfiguration des projekts zum bauen auf windows | cloud |
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LearningWebAppDev | learningwebappdev examples from the book learning web application development from o reilly here s the book website http learningwebappdev com | front_end |
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recruiting | recruiting symmetry technology recruiting information | server |
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AR-Indoor-Navigation-System | ar indoor navigation system indoor navigation using unity and vuforia sdk prototype at faculty of information technology kmitl | server |
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LLMBar | evaluating large language models at evaluating instruction following this repository contains the data and code for paper evaluating large language models at evaluating instruction following https arxiv org abs 2310 07641 in this paper we introduce a challenging meta evaluation benchmark llmbar designed to test the ability of an llm evaluator in discerning instruction following outputs llmbar consists of 419 instances where each entry contains an instruction paired with two outputs one faithfully and correctly follows the instruction and the other deviates from it there is also a gold preference label indicating which output is objectively better for each instance quick links requirements requirements data data code structure code structure reproducing baselines reproducing baselines bug or questions bug or questions citation citation requirements please install the packages by pip install r requirements txt this codebase has been tested with python 3 10 4 data all the data are stored in dataset the natural set of llmbar is stored in dataset natural the four subsets of llmbar adversarial set are stored in dataset llmbar adversarial neighbor gptinst gptout manual the five evaluation subsets we studied in 4 6 case study a more challenging meta evaluation set are stored in dataset casestudy constraint negation normal base 9 base 10 we also evaluate llm evaluators on faireval llmeval 2 and mt bench we remove llmeval 2 instances whose instructions are empty or non english and add the task description before the raw input to get the instruction for mt bench we get the gold preferences by majority vote we remove all tie instances and randomly sample 200 instances for llmeval 2 and mt bench respectively the processed data are stored in dataset processed faireval llmeval 2 mt bench all the evaluation instances in each folder are stored in dataset json each instance is a json object with the format json input infer the implied meaning of the following sentence she is not what she used to be output 1 she is not as she once was output 2 she has changed substantially over time label 2 input is the input instruction output 1 and output 2 are the two evaluated outputs o 1 and o 2 respectively label is either 1 or 2 indicating which output is objectively better code structure all the codes are stored in llmevaluator evaluate py run file to reproduce our baselines evaluators config folder that contains all config files to reproduce baselines evaluators prompts folder that contains all prompt files run llm evaluators you can reproduce llm evaluators from our paper by bash cd llmevaluator python evaluate py path path to data folder evaluator base llm prompting strategy num procs number of processes the default value of num procs is 10 see the following content for more arguments base llm is one of gpt 4 chatgpt llama2 palm2 falcon and chatgpt 0301 if you use gpt 4 chatgpt or chatgpt 0301 you will also need to pass the openai api arguments bash api type your api type api version your api version api base your api base api key your api key organization your organization if you use azure api you may need to pass api type api version api base and api key otherwise you may need to pass api key and organization also ensure that the arguments in the config files align with those expected by the function if you use palm2 you will also need to pass the palm api key bash palm api key your palm api key if you use llama2 llama 2 70b chat https huggingface co meta llama llama 2 70b chat hf or falcon falcon 180b chat https huggingface co tiiuae falcon 180b chat you will also need to pass the hugging face authorization token please make sure your account has the access to the model bash hf use auth token your use auth token an example of the command bash python evaluate py path dataset llmbar natural evaluator gpt 4 vanilla api type azure api version 2023 05 15 api base your api base api key your api key the current list of prompting strategy check out our paper for more details includes vanilla norules vanilla vanilla vanilla vanilla rules vanilla 1shot vanilla vanilla rules w 1 shot in context learning vanilla 2shot vanilla vanilla rules w 2 shot in context learning cot cot cot rules metrics metrics rules metrics reference reference rules reference metrics reference metrics reference rules metrics reference swap swap rules swap swap cot swap cot rules swap cot rating norules vanilla w the rating approach rating vanilla vanilla rules w the rating approach rating metrics metrics rules metrics w the rating approach rating reference reference rules reference w the rating approach rating metrics reference metrics reference rules metrics reference w the rating approach after running the code the results will be stored in path to data folder evaluators base llm prompting strategy result json is the intermediate results for evaluating the llm evaluators on all instances statistics json is the final statistics of the evaluation where correct average and equal represent average accuracy acc and positional agreement rate agr respectively we have already put our results reported in our paper in the repository bug or questions if you have any questions related to the code or the paper feel free to email zhiyuan zeng zhiyuan1zeng gmail com or zengzy20 mails tsinghua edu cn if you encounter any problems when using the code or want to report a bug you can open an issue please try to specify the problem with details so we can help you better and quicker citation please cite our paper if you use this repo in your work bibtex article zeng2023llmbar title evaluating large language models at evaluating instruction following author zeng zhiyuan and yu jiatong and gao tianyu and meng yu and goyal tanya and chen danqi journal arxiv preprint arxiv 2310 07641 year 2023 | ai |
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ae6310 | ae6310 this repository contains notebooks from the course ae6310 optimization for the design of engineered systems at georgia tech in the school of aerospace engineering | os |
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letsgojohor | letsgojohor a final year project for bachelor of computer science software engineering developed using android studio and cloud firestore it is a travel planning and information application | cloud |
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cetec | cetec computer engineering database tecnol gico de costa rica | server |
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SOFTWARE-ENGINEERING-INVENTORY-P.O.S.-MANAGEMENT | software engineering inventory p o s management cse3001 software engineering this repository contains the project including suggestion guidelines associated to professor prof akila victor this project is a web application that is implemented using php mysql database school of computer science engineering cse 3001 software engineering project report inventory p o s management submitted by punit middha 19bce2060 vansh aggarwal 19bce0987 srivathsan b nayak 19bce2015 under the guidance of prof akila victor scope | server |
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Front-end-Developer-Interview-Questions | front end developer interview questions this repository contains a number of front end interview questions that can be used when vetting potential candidates it is by no means recommended to use every single question here on the same candidate that would take hours choosing a few items from this list should help you vet the intended skills you require note keep in mind that many of these questions are open ended and could lead to interesting discussions that tell you more about the person s capabilities than a straight answer would you can read more about this project its history here https h5bp org front end developer interview questions about table of contents 1 general questions src questions general questions md 2 html questions src questions html questions md 3 css questions src questions css questions md 4 js questions src questions javascript questions md 5 accessibility questions https scottaohara github io accessibility interview questions external link 6 testing questions src questions testing questions md 7 performance questions src questions performance questions md 8 network questions src questions network questions md 9 coding questions src questions coding questions md 10 fun questions src questions fun questions md getting involved 1 contributors contributors 2 how to contribute https github com h5bp front end developer interview questions blob master github contributing md 3 license https github com h5bp front end developer interview questions blob master license md the project is currently maintained by roblarsen https github com roblarsen contributors feeling inspired check our contributing guide https github com h5bp front end developer interview questions blob master github contributing md to get started all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href http darcyclarke me img src https avatars2 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deksnis br sub b harijs deksnis b sub a br a href translation arcanous title translation a td td align center a href http htmlcssjavascript com img src https avatars0 githubusercontent com u 361421 v 4 width 120px alt rob larsen br sub b rob larsen b sub a br a href ideas roblarsen title ideas planning feedback a a href review roblarsen title reviewed pull requests a a href maintenance roblarsen title maintenance a td td align center a href https cezaraugusto net img src https avatars0 githubusercontent com u 4672033 v 4 width 120px alt cezar augusto br sub b cezar augusto b sub a br a href ideas cezaraugusto title ideas planning feedback a a href infra cezaraugusto title infrastructure hosting build tools etc a a href review cezaraugusto title reviewed pull requests a a href maintenance cezaraugusto title maintenance a td tr tr td align center a href https www linkedin com in vvanchuk img src https avatars1 githubusercontent com u 6904368 v 4 width 120px alt vasiliy vanchuk br sub b vasiliy vanchuk b sub a br a href ideas vvscode title ideas planning feedback a a href review vvscode title reviewed pull requests a a href maintenance vvscode title maintenance a td tr table markdownlint enable prettier ignore end all contributors list end license copyright c contributors of the front end developer interview questions https github com h5bp front end developer interview questions blob master license md | interview-questions css-questions js-questions html-questions front-end interview-test | front_end |
knowledge | build status https travis ci org ecomfe knowledge png https travis ci org ecomfe knowledge http knowledge ecomfe com docs intro md docs contribute md docs roadmap md | front_end |
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applied-nlp | applied natural language processing this is the course repository for applied natural language processing with stubs for homeworks for more information see the course wiki https github com utcompling applied nlp wiki there is also a class mailing list https groups google com forum hl en fromgroups forum ut austin applied nlp individuals who are not in the class at ut austin are welcome to join in the discussion requirements version 1 6 of the java 2 sdk http java sun com configuring your environment variables the easiest thing to do is to set the environment variables java home and anlp dir to the relevant locations on your system set java home to match the top level directory containing the java installation you want to use next add the directory anlp dir bin to your path for example you can set the path in your bashrc file as follows export path path anlp dir bin | ai |
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ytel | ytel ytel is an experimental youtube frontend for emacs it s goal is to allow the user to collect the results of a youtube search in an elfeed like buffer and then manipulate them with emacs lisp the gif below shows that ytel can be used to play videos in an external player to learn how to emulate it refer to the usage usage section below p align center img src https github com grastello ytel blob master pic demonstration gif p this project was inspired by elfeed https github com skeeto elfeed and invidious https github com omarroth invidious it does indeed use the invidious apis installation this project is on melpa https melpa org you should be able to m x package install ret ytel another option is to clone this repository under your load path dependencies while ytel does not depend on any emacs package it does depend on curl so if you happen not to have it install it through your package manager meme distros aside it is probably in your repos usage once everything is loaded m x ytel creates a new buffer and puts it in ytel mode this major mode has just a few bindings for now key binding key n key next line key p key previous line key q key ytel quit key s key ytel search key key ytel search next page key key ytel search previous page pressing s will prompt for some search terms and populate the buffer once the results are available one can access information about a video via the function ytel get current video that returns the video at point videos returned by ytel get current video are cl structures so you can access their fields with the ytel video functions currently videos have four fields field description id the video s id title the video s title author name of the author of the video authorid id of the channel that updated the video length length of the video in seconds views number of views published date of publication unix style timestamp with this information we can implement a function to stream a video in mpv provided you have youtube dl installed as follows elisp defun ytel watch stream video at point in mpv interactive let video ytel get current video id ytel video id video start process ytel mpv nil mpv concat https www youtube com watch v id ytdl format bestvideo height 720 bestaudio best message starting streaming and bind it to a key in ytel mode with elisp define key ytel mode map y ytel watch this is of course just an example you can similarly implement functions to open a video in the browser download a video download just the audio of a video by relying on the correct external tool it is also possible to customize the sorting criterion of the results by setting the variable ytel sort criterion to one of the following symbols relevance rating upload date or view count the default value is relevance potential problems and potential fixes ytel does not use the official youtube apis but relies on the invidious https github com omarroth invidious apis that in turn circumvent youtube ones the variable ytel invidious api url points to the invidious instance by default https invidio us to use you might not need to ever touch this but if invidio us https invidio us goes down keep in mind that you can choose another instance https github com omarroth invidious invidious instances moreover the default invidious instance is generally speaking stable but sometimes your ytel search might hang in that case c g and retry currently some wide unicode characters such as chinese japanese korean characters are very likely to mess up the ytel buffer the messing up is not that bad but things will not be perfectly aligned fixing this problem will most likely require a rewrite of how the ytel buffer is actually drawn we re somewhat working on it extra there s an extension https github com xfa25e ytel show to browse comments and video information contributing feel free to open an issue or send a pull request i m quite new to writing emacs packages so any help is appreciated faq why one can easily subscribe to youtube channels via an rss feed and access it in emacs via elfeed https github com skeeto elfeed but sometimes i want to search youtube for videos of people i don t necessarily follow e g for searching a tutorial or music or wasting some good time and being able to do that without switching to a browser is nice what about helm youtube https github com maximus12793 helm youtube and ivy youtube https github com squiter ivy youtube first of all those packages require you to get a google api key while ytel uses the invidious https github com omarroth invidious apis that in turn do not use the official google apis moreover those packages are designed to select a youtube search result and play it directly in your browser while ytel is really a way to collect search results in an elfeed like buffer and make them accessible to the user via functions such as ytel get current video so the user gets to decide what to to with them | emacs youtube-search youtube | front_end |
machinelearning | machine learning for net ml net https dotnet microsoft com apps machinelearning ai ml dotnet is a cross platform open source machine learning ml framework for net ml net allows developers to easily build train deploy and consume custom models in their net applications without requiring prior expertise in developing machine learning models or experience with other programming languages like python or r the framework provides data loading from files and databases enables data transformations and includes many ml algorithms with ml net you can train models for a variety of scenarios https docs microsoft com dotnet machine learning resources tasks like classification forecasting and anomaly detection you can also consume both tensorflow and onnx models within ml net which makes the framework more extensible and expands the number of supported scenarios getting started with machine learning and ml net learn more about the basics of ml net https dotnet microsoft com apps machinelearning ai ml dotnet build your first ml net model by following our ml net getting started tutorial https dotnet microsoft com learn ml dotnet get started tutorial intro check out our documentation and tutorials https docs microsoft com dotnet machine learning see the api reference documentation https docs microsoft com dotnet api view ml dotnet clone our ml net samples github repo https github com dotnet machinelearning samples and run some sample apps take a look at some ml net community samples https github com dotnet machinelearning samples blob main docs community samples md watch some videos on the ml net videos youtube playlist https aka ms mlnetyoutube roadmap take a look at ml net s roadmap roadmap md to see what the team plans to work on in the next year operating systems and processor architectures supported by ml net ml net runs on windows linux and macos using net core or windows using net framework ml net also runs on arm64 apple m1 and blazor web assembly however there are some limitations docs project docs platform limitations md 64 bit is supported on all platforms 32 bit is supported on windows except for tensorflow and lightgbm related functionality ml net nuget packages status nuget status https img shields io nuget vpre microsoft ml svg style flat https www nuget org packages microsoft ml release notes check out the release notes docs release notes to see what s new you can also read the blog posts https devblogs microsoft com dotnet category ml net for more details about each release using ml net packages first ensure you have installed net core 2 1 https www microsoft com net learn get started or later ml net also works on the net framework 4 6 1 or later but 4 7 2 or later is recommended once you have an app you can install the ml net nuget package from the net core cli using dotnet add package microsoft ml or from the nuget package manager install package microsoft ml alternatively you can add the microsoft ml package from within visual studio s nuget package manager or via paket https github com fsprojects paket daily nuget builds of the project are also available in our azure devops feed https pkgs dev azure com dnceng public packaging dotnet libraries nuget v3 index json https pkgs dev azure com dnceng public packaging dotnet libraries nuget v3 index json building ml net for contributors building ml net open source code to build ml net from source please visit our developer guide docs project docs developer guide md codecov https codecov io gh dotnet machinelearning branch main graph badge svg flag production https codecov io gh dotnet machinelearning debug release centos build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname centos x64 net60 configuration centos x64 net60 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname centos x64 net60 configuration centos x64 net60 20release build https dev azure com dnceng public build latest definitionid 104 branchname main ubuntu build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname ubuntu x64 net60 configuration ubuntu x64 net60 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname ubuntu x64 net60 configuration ubuntu x64 net60 20release build https dev azure com dnceng public build latest definitionid 104 branchname main macos build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname macos x64 net60 configuration macos x64 net60 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname macos x64 net60 configuration macos x64 net60 20release build https dev azure com dnceng public build latest definitionid 104 branchname main windows x64 build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x64 net60 configuration windows x64 net60 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x64 net60 configuration windows x64 net60 20release build https dev azure com dnceng public build latest definitionid 104 branchname main windows fullframework build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x64 netfx461 configuration windows x64 netfx461 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x64 netfx461 configuration windows x64 netfx461 20release build https dev azure com dnceng public build latest definitionid 104 branchname main windows x86 build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x86 net60 configuration windows x86 net60 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x86 net60 configuration windows x86 net60 20release build https dev azure com dnceng public build latest definitionid 104 branchname main windows netcore3 1 build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x64 net60 configuration windows x64 net60 20debug build https dev azure com dnceng public build latest definitionid 104 branchname main build status https dev azure com dnceng public apis build status dotnet machinelearning machinelearning ci branchname main jobname windows x64 net60 configuration windows x64 net60 20release build https dev azure com dnceng public build latest definitionid 104 branchname main release process and versioning major releases of ml net are shipped once a year with the major net releases starting with ml net 1 7 in november 2021 with net 6 then ml net 2 0 with net 7 etc we will maintain release branches to optionally service ml net with bug fixes and or minor features on the same cadence as net servicing check out the release notes docs release notes to see all of the past ml net releases contributing we welcome contributions please review our contribution guide contributing md community join our community on discord https aka ms dotnet discord tune into the net machine learning community standup https dotnet microsoft com live community standup every other wednesday at 10am pacific time this project has adopted the code of conduct defined by the contributor covenant https contributor covenant org to clarify expected behavior in our community for more information see the net foundation code of conduct https dotnetfoundation org code of conduct code examples here is a code snippet for training a model to predict sentiment from text samples you can find complete samples in the samples repo https github com dotnet machinelearning samples c var datapath sentiment csv var mlcontext new mlcontext var loader mlcontext data createtextloader new new textloader column sentimenttext datakind string 1 new textloader column label datakind boolean 0 hasheader true separatorchar var data loader load datapath var learningpipeline mlcontext transforms text featurizetext features sentimenttext append mlcontext binaryclassification trainers fasttree var model learningpipeline fit data now from the model we can make inferences predictions c var predictionengine mlcontext model createpredictionengine sentimentdata sentimentprediction model var prediction predictionengine predict new sentimentdata sentimenttext today is a great day console writeline prediction prediction prediction license ml net is licensed under the mit license license and it is free to use commercially net foundation ml net is a part of the net foundation https www dotnetfoundation org projects | machine-learning algorithms dotnet ml | ai |
Python-Machine-Learning-Algorithm | python http blog csdn net google19890102 article details 77996085 http img10 360buyimg com n1 jfs t6391 74 1115083732 144183 2a82437f 594b5bb8na3c6dfd4 jpg python https item jd com 12109305 html http product dangdang com 25100931 html https www amazon cn python e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e7 ae 97 e6 b3 95 e8 b5 b5 e5 bf 97 e5 8b 87 dp b072zcxmrn ref sr 1 2 ie utf8 qid 1498627300 sr 8 2 keywords python e6 9c ba e5 99 a8 e5 ad a6 e4 b9 a0 e7 ae 97 e6 b3 95 6 python http img blog csdn net 20170705094315143 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094330627 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094454527 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094511813 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094527619 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094546703 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094612914 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094627711 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094651158 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094718211 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast http img blog csdn net 20170705094736571 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast qq http img blog csdn net 20170704222450090 watermark 2 text ahr0cdovl2jsb2cuy3nkbi5uzxqvz29vz2xlmtk4otaxmdi font 5a6l5l2t fontsize 400 fill i0jbqkfcma dissolve 70 gravity southeast | ai |
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Computer_Engineering_UTFPR-PB | computer engineering utfpr pb some of the projects labs algorithms and data structures developed during my bachelor degree in computer engineering at utfpr pb website utfpr pb engenharia de computa o http portal utfpr edu br cursos coordenacoes graduacao pato branco pb engenharia de computacao | computer computer-engineering enginneering utfpr utfpr-pb utfpr-pato-branco | server |
llm-hallucination-eval | copyright c 2023 yurts ai this software is released under the mit license https opensource org licenses mit llm hallucination evaluation yurtsai yurtsai developed a pipeline to evaluate the famous hallucination problem of large language models refer to illusions unraveled the magic and madness of hallucinations in llms part 1 blog to learn more about hallucinations and the evaluation pipeline evaluation pipeline eval pipeline wrench setup requirements python 3 10 poetry poetry hugs huggingface token hf token firstly create a virtual environment and activate it sh python3 10 m virtualenv venv source venv bin activate to install the required dependencies assuming you have poetry installed run it also logs you into hugs hub which prompts for your hugs hub token sh make install or in dev mode sh make install dev gear evaluation to evaluate the model on the given techcrunch dataset run sh python m llm eval model name or path tiiuae falcon 7b instruct max length 512 data max size 100 num proc 4 batch size 8 compute reward for more information run llm eval help or python m llm eval help some models have different input formatting i e addition of special token or formatted in certain ways to handle this you can use the input format flag for example to preprocess the input for the openassistant falcon 7b sft mix 2000 model run sh python m llm eval model name or path openassistant falcon 7b sft mix 2000 data max size 100 input format prompter endoftext assistant batch size 8 shuffle max length 512 compute reward bar chart visualize if you d like further data exploration you can use pandas or your favorite data analysis library to visualize the data if you re not familiar with pandas you can use the following snippet make sure to pip install pandas first python import pandas as pd load the data to a pandas dataframe df pd read json res eval falcon 7b instruct tech crunch jsonl lines true filter type 1 hallucinations good df df reward 1 neutral df df reward 0 bad df df reward 0 get the number of good neutral and bad responses n n good n neutral n bad len df len good len neutral len bad print f good n good n good n 2 print f neutral n neutral n neutral n 2 print f bad n bad n bad n 2 you re welcome to submit a pull request with your visualizations technologist contribution you are very welcome to modify and use them in your own projects please keep a link to the original repository if you have made a fork with substantial modifications that you feel may be useful then please open a new issue on github issues with a link and short description balance scale license mit this project is opened under the mit license which allows very broad use for both private and commercial purposes a few of the images used for demonstration purposes may be under copyright these images are included under the fair usage laws poetry https python poetry org docs tech crunch https techcrunch com yurtsai https yurts ai blog https medium com yurts illusions unraveled the magic and madness of hallucinations in llms part 1 61d988e704b hf token https huggingface co docs hub security tokens eval pipeline res images eval pipeline png pull request https github com yurtsai llm hallucination eval pulls original repository https github com yurtsai llm hallucination eval issues https github com yurtsai llm hallucination eval issues license license | ai |
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stacker | p align center img alt frameworks a lot he s one src logo png p latest stable version https poser pugx org maxlab stacker v stable https packagist org packages maxlab stacker build status https travis ci org maxlab stacker svg branch master https travis ci org maxlab stacker license https poser pugx org maxlab stacker license https packagist org packages maxlab stacker latest unstable version https poser pugx org maxlab stacker v unstable https packagist org packages maxlab stacker introduction english https github com maxlab stacker https github com maxlab stacker blob master readme cn md why stacker stacker this is a local environment for web development with everything you need what is its benefit 1 you do not need to manually configure the web server and add to the hosts just cloned it and immediately launched it in the browser it looks so demo https youtu be 42bemufk5 4 2 inside there is already everything that is needed in 90 of all cases and if not we will add it for you 3 for you there is a super zsh console with autocomplete and everything you need video with presentation https youtu be n7hpponcaa4 list pld8vgb8i9tyha8yod dev6bx5hzco0zgy 4 there is an autocompletion for symfony and laravel commands out of the box for example la5 and a double tab will output a list of commands for which you can walk with arrows to select them 5 it is faster analogs the same homestead is just a turtle compared to it 6 there is a video course https www youtube com playlist list pld8vgb8i9tyha8yod dev6bx5hzco0zgy 7 friendly author in case there are questions or suggestions 8 based on docker wherever you can install docker you can install stacker 9 it is very simple to expand the process of adding your own images with a couple of lines in docker compose yml 10 just try it general goals frameworks a lot he s one everything is easy nothing to migrate quickly start of developing locally no overhead on settings opied project and run zoo under a docker let the host mashine remains clean video demos ru presentation https youtu be qvqzymczuwm phpstorm xdebug stacker profit https youtu be rynramdzj q console composer gulp npm gem bower https youtu be wbfms35ucfk run symfony laravel and native php scripts https youtu be tonmezpuqkc requirements install docker https docs docker com install docker compose https docs docker com compose install 1 8 0 installation get a stacker sh composer create project maxlab stacker or git clone git github com maxlab stacker git run in stacker directory sh make workspace folder and make a symbolic link to your folder with all your projects mkdir workspace ln s your path to all your own projects workspace copy env dist to env and change it cp env dist env docker compose build docker compose up d docker compose ps mv test workspace set local dns server sh linux etc resolv conf mac system settings windows network adapter setting set your local dns server to 127 0 0 1 to prevent dnsmasq from running you need to set up the second dns server such as 8 8 8 8 or something else then open http test php dev in your browser examples https youtu be 42bemufk5 4 for ssh copy your ssh keys in the folder workspace sh cp r ssh www docker stacker workspace move your projects add your project in workspace folder workspace customer projectname no need to restart this will work out of the box open http customer project dev in your browser if you do not have dnsmasq you have to add your hosts file manually on the ship mailcatcher schickling mailcatcher latest all outgoing mail is sent to http mail dev nginx nginx 1 10 1 elasticsearch elasticsearch 5 mysql mysql 5 7 pgsql postgres 9 6 php7xdebug php 7 1 xdebug dnsmasq dnsmasq latest php5apache php 5apache for legacy php7console stacker console redis redis 3 0 console zsh oh my zsh http ohmyz sh for frontend nodejs gem npm webpack bower gulp uglify js uglifycss for backend composer php phpunit symfony symfony autocomplete yii2 autocomplete for automation deploy dep deployer http deployer org faq which settings in the configs for my projects database you can access the database in your app config use db for mysql and pgsql for postgresql files will be saved in the mysql directory so it will be saved after destroying or recreating the containers yaml example for mysql parameters database host mysql database port 3306 database name sf database user root database password root example for pgsql parameters database host pgsql database port 5433 database name sf database user postgres database password postgres example for redis parameters database host redis database port 6379 what external ports are listening images it s easy for convenience the external ports of the databases are offset by plus one for example mysql listens to port 3306 1 3307 and so on check the file docker compose yml docker compose yml for more xdebug phpstorm configuration watch this video https youtu be rdmcgaaqgfi in russian 1 go to settings languages frameworks php 2 click the behind your interperter i have a lot of the symfony project is it possible to make a symbolic link to them yes it s much faster and easier plus no need to move folders from the usual places in the directory with your projects create a folder and copy all the projects from the symfony code now make a link to your directory project in the directory with the stacker remove a directory workspace and rename your link to workspace that s all now all your symfony projects is available from the browser how to contact the any instances staker in console you can do so sh your path to stacker folder bin stacker console but it will be much better sh for bash echo export path your path to stacker folder bin path bashrc source bashrc for zshrc echo export path your path to stacker folder bin path zshrc source zshrc then restart console and run stacker console symfony completion sh stacker console cd to symfony folder sf tab 2 for sf3 completion or sf2 for sf2 completion laravel5 completion sh stacker console cd to symfony folder la5 tab 2 commands sh stacker usage for list available commands stacker console for enter to console stacker logs cont name f for logs stream container stacker build stacker down stacker up stacker ps for full rebuild support project you can support the project in several ways 1 becoming a sponsor if you are interested in becoming a sponsor please visit the stacker patreon page http patreon com maxlab 2 posting review you can support the project by posting reviews in their social networks send a link to the review and we ll post it here 3 buy a beer gratipay user https img shields io gratipay user maxlab svg https gratipay com maxlab bountysource https img shields io bountysource team maxlabstacker activity svg https www bountysource com teams maxlabstacker donate https img shields io badge donate paypal green svg https www paypal com cgi bin webscr cmd s xclick hosted button id q477vjvb9stgs | symfony php xdebug laravel pgsql mysql redis nginx workspace mailcatcher dnsmasq lamp xampp rad docker-compose php-container yii2 zsh symfony3 docker | front_end |
IIITT-library | iiitt library library management system for indian institute of information technology tiruchirappalli by mayukh pankaj try test vesion hosted on 000webhost http iiit 000webhostapp com php mysql css html5 have been used for development hosted on local apache server first version was deployed on 5th nov 2019 features include issue book return book search records check number of books already issued days since issued fine calculator paytm for fine payment composer used barcode integration are still to be added a project for my college library preview https i imgur com 0jq49dj gif | php mysqli composer paytmsdk | server |
nlp | nlp natural language processing repo neural machine translation https github com 6chaoran nlp tree master nmt seq2seq model | nlp | ai |
hector | hector golang machine learning lib currently it can be used to solve binary classification problems supported algorithms 1 logistic regression 2 factorized machine 3 cart random forest random decision tree gradient boosting decision tree 4 neural network dataset format hector support libsvm like data format following is an sample dataset 1 1 0 7 3 0 1 9 0 4 0 2 0 3 4 0 9 7 0 5 0 2 0 7 5 0 3 how to run run as tools hector cv go will help you test one algorithm by cross validation in some dataset you can run it by following steps go get github com xlvector hector go install github com xlvector hector hectorcv hectorcv method method train data path cv 10 here method include 1 lr logistic regression with sgd and l2 regularization 2 ftrl ftrl proximal logistic regreesion with l1 regularization please review this paper for more details ad click prediction a view from the trenches 3 ep bayesian logistic regression with expectation propagation please review this paper for more details web scale bayesian click through rate prediction for sponsored search advertising in microsoft s bing search engine 4 fm factorization machine 5 cart classifiaction tree 6 cart regression regression tree 7 rf random forest 8 rdt random decision trees 9 gbdt gradient boosting decisio tree 10 linear svm linear svm with l1 regularization 11 svm svm optimizaed by smo current its linear svm 12 l1vm vector machine with l1 regularization by rbf kernel 13 knn k nearest neighbor classification hector run go will help you train one algorithm on train dataset and test it on test dataset you can run it by following steps cd src go build hector run go hector run method method train data path test data path above methods will direct train algorithm on train dataset and then test on test dataset if you want to train algorithm and get the model file you can run it by following steps hector run method method action train train data path model model path then you can use model file to test any test dataset hector run method method action test test data path model model path benchmark binary classification following are datasets used in benchmarks you can find them from uci machine learning repository http archive ics uci edu ml 1 heart 2 fourclass i will do 5 fold cross validation on the dataset and use auc as evaluation metric following are the results dataset method auc heart ftrl lr 0 9109 heart ep lr 0 8982 heart cart 0 8231 heart rdt 0 9155 heart rf 0 9019 heart gbdt 0 9061 fourclass ftrl lr 0 8281 fourclass ep lr 0 7986 fourclass cart 0 9832 fourclass rdt 0 9925 fourclass rf 0 9947 fourclass gbdt 0 9958 | ai |
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Assesment-2-repository | assesment 2 repository | server |
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database_engineering_capstone_project | database engineering capstone project this project is a part of the database engineering professional certificate by meta it contains mainly the following implementations 1 setting up the project first we must create a data model and database schema for our project it has been done through mysql workbench 2 adding sales reports some sales reports are generated through multiple joins prepared statements stored procedures subqueries and views using mysql 4 table booking system 5 data visualization 6 database client | server |
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Embedded-Diploma | super guacamole | c embedded-c microcontrollers socs mcu-drivers | os |
SwampCooler2560 | div align center swampcooler2560 a state managed swamp cooler targeting the arduino mega 2560 https docs arduino cc hardware mega 2560 img src https raw githubusercontent com stehfyn swampcooler2560 c500ab42bb0f8f0503ba2b2f96ac1333bf7baf19 assets demo png div external dependencies p a href https github com arduino libraries liquidcrystal style text decoration line none liquidcrystal a a href https github com winlinvip simpledht style text decoration line none simpledht a a href https github com northernwidget ds3231 style text decoration line none ds3231 a a href https github com palladinomarco alignedjoystick style text decoration line none alignedjoystick a p usage after installing swampcooler2560 and its external dependencies simply include the main header cpp include swampcooler2560 to instantiate with the default pin map cpp swampcooler2560 sc or a custom one cpp swampcooler2560 sc swampcooler2560pinmap 7 power switch a0 joystick right delta a1 joystick left delta 3 disabled led 4 idle led 5 running led 6 error led 35 lcd register select 33 lcd enable the next four parameters are for the lcd s data bits in 4 bit mode ordered from least significant to most significant 31 lcd data bit least significant bit 29 lcd data bit 27 lcd data bit 25 lcd data bit most significant bit 22 dht11 data a2 water sensor data sda i2c serial data sda scl i2c serial clock scl 2 fan motor dc driven 13 servo motor ac driven and finally cpp void setup sc begin void loop sc update | os |
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workwell | workwell sdk npm version https img shields io npm v workwell svg https www npmjs com package workwell build status https travis ci org workwell workwell svg branch master https travis ci org workwell workwell workwell is a front end framework to communicate between a web application and the workwell mobile application it also provides an easy to use mobile like ios android ui framework created by the workwell https www workwell io engineering team table of contents introduction introduction documentation documentation introduction put your service between the hands of thousands of employees by doing a workwell integration wether you already have a web service or you re building one from scratch workwell provides the necessary tools to make the integration easy and fast br p align center img src docs images phone 2x png alt drawing width 300px p br technical concept before your start diving into the technical details we wanted to let you know about our general technical concept workwell is a native mobile application services are web apps presented to the user on a dedicated screen opening a service is done by loading its url in a in app custom webview using the workwell js sdk your service can communicate with the workwell native application here are the different components inside a service p align center img src docs images service components svg alt drawing width 600px p workwell sdk workwell sdk allows you to get data about the user to use native features e g camera chat etc to use native like ui components that handle the differences between os and android for you to use native ui components to give the user experience a more native flavour e g date time pickers toast messages etc integration requirements we have put in place a number of requirements for a workwell integration to ensure the best user experience with a strong attention to the security aspects of our technical model automatic user identification by using workwell users can access all the services provided by their companies they shouldn t need to manually login in or create accounts when opening your service use the js sdk to get the user s informations so you can behind the hood automatically create an account or log the user in user should never therefore have to login to use your service mobile friendly we require your service to be mobile friendly and to respect workwell ui guidelines we strongly recommend you to use the js sdk ui features expose an authorization endpoint your server needs to expose an endpoint workwell servers can call to verify your service health and its authenticity c f service authorization docs service authorization md we also recommend using classic navigation with a href and not spa single page application router a native app opens a new view page when navigating from one content to another cf open web page docs js sdk md openwebpage for more information documentation getting started docs getting started md 1 get the workwell mobile test app docs getting started md workwell mobile test 2 access your local or online web app docs getting started md access web app 3 get a service secret and a service id docs getting started md service secret id 4 implement a service token generation method docs getting started md service token 5 build your first workwell web app docs getting started md build first debugging docs debugging md ios docs debugging md ios android docs debugging md android service authorization docs service authorization md service token generation examples docs service token examples md ui guidelines docs ui guidelines md ui components docs ui components md js sdk docs js sdk md bridge with the app docs js sdk md bridge with the app ui docs js sdk md ui workwell api docs api md get user info docs api md user info send push notification to user site company docs api md push notification post on user site company timeline docs api md post timeline update timeline docs api md update timeline delete timeline docs api md delete timeline workwell api error codes docs api md error code faq docs faq md creators zachary lithgow https github com zlithgow | os |
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uwpcore.framework | div align center img src https github com bsautermeister uwpcore framework blob master assets uwpcore png alt uwpcore framework br div uwpcore framework for windows 10 mobile the uwpcore framework is a development acceleration library for the universal windows platform it is a collection of best practices and reusable services to simplify the development for windows 10 apps is the framework inspired by another one the framworks navigation system and application shell is maily inspired by template 10 https github com windows xaml template10 by jerry nixon the application shell has been further improved with swipe gestured inspired by justin xin liu https github com justinxinliu swipeablesplitview from time to time the ui of the shell has been updated to be more similar to microsofts default apps such as news weather sports or groove music how to use this framework 1 check out the repository 2 launch the uwpcore framework sln solution in visual studio 3 optional export a project template based on the uwpcore template project to get started even faster 1 select file export template 2 select project template as well as the template uwpcore template project 3 give it a handy name and use auto import to visual studio 4 create a new universal windows project in visual studio and use the previously exported template 5 add the framework project as an existing project to be able to debug within the code of the framework otherwise it is sufficient to reference the dlls of the framework 6 update the project reference to uwpcore framework 7 important clean and recompile the whole solution 8 you are now ready to get started 1 don t forget to modify the manifeest as well as the string resources accoding to your personal needs the generated app includes a home page a settings page including the functionality to switch the app theme as well as an about page furthermore it uses app localization english and germany and the mvvm pattern is there anything i should know before i can hack down my app it might be helpful to read the folowing sections first before you start with coding app the app class is the root of your app it inherits from uwpcore framework common universalapp which extends and simplifies the application base class of a standard uwp project it provides for example dirct access to the navigationservice to navigate to a page its constructor requires some important information that has to be defined csharp public app base typeof mainpage appbackbuttonbehaviour keepalive true new defaultmodule initializecomponent the parameters of the universalapp constructor define the starting page the behavior of the back button on the root page of the stack whether to use the appshell hamburger menu or not as well as multiple module definition instances for used di framework ninject http www ninject org within the oninitialize iactivatedeventargs method we can initialize the app such as defining the theme colors csharp public async override task oninitializeasync iactivatedeventargs args await base oninitializeasync args setup theme colors mainly for title bar colorpropertiesdark new appcolorproperties appconstants color accent colors white colors black colors white color fromargb 255 31 31 31 null null colorpropertieslight new appcolorproperties appconstants color accent colors black colors white colors black color fromargb 255 230 230 230 null null within the onstartasync startkind iactivatedeventargs method the app gets started activated and we can select the page we would like to navigate to this method is lauched even when the app is launched using cortana live tiles or toast notifications check the start kind and the event arguments to handle the app s startup properly csharp public override task onstartasync startkind startkind iactivatedeventargs args var pagetype defaultpage object parameter null start the user experience navigationservice navigate pagetype parameter return task fromresult object null in case we are using the appshell by using true for the thrid parameter in the universalapp constructor it is required to override the createnavigationmenuitems and createbottomdockednavigationmenuitems methods which populate the navigation menu in the app xaml file make sure that you specify the proper theme colors here as well this is required that even the default controls of the windows universal platform use these theme colors such as selected items of a listview control xml resourcedictionary themedictionaries resourcedictionary x key dark color x key systemaccentcolor 0063b1 color resourcedictionary resourcedictionary x key light color x key systemaccentcolor 0063b1 color resourcedictionary resourcedictionary themedictionaries pages every page is placed within the views folder since it is recommended to use the mvvm pattern the code behind of each page only assignes the proper view model to the data context all the magic of a page lives in the view model implementation located in the viewmodels folder viewmodels each view model inherits from wpcore framework mvvm viewmodelbase and offers public properties where the view is able to bind to for the main logic we recommend to encapsulate this functionality in services to be able to share these across multiple view models these service implementations can then be injected using ninject csharp private idialogservice dialogservice public mainviewmodel dialogservice injector get idialogservice feel free in case you prefer to inject the view model implementations as well to improve the testability of the view model in this case you have to create your own di module definition and add this to the last constructor parameter of the universalapp base class in app xaml cs services in our opinion the mvvm pattern is lacking the recommendation of using services to share functionality accross view models and pages some people prefer the terminology mvvms pattern each service is just a simple class that offers some functionality a service can be composed of other services which can be simply injected using the inject attribute of ninject csharp public class tilepinservice itilepinservice private itileservice tileservice private ideviceinfoservice deviceinfoservice inject public tilepinservice itileservice tileservice ideviceinfoservice deviceinfoservice tileservice tileservice deviceinfoservice deviceinfoservice to be able to inject our new service we have to include it in our custom module definition class csharp public class appmodule ninjectmodule public override void load services bind itilepinservice to tilepinservice insingletonscope make sure you add this custom module definition to the applications constructor csharp public app base typeof mainpage appbackbuttonbehaviour keepalive true new defaultmodule new appmodule | framework uwp windows-10 | front_end |
AI-social-ethical-issues | course social and ethical issues in information technology the course aims to provide a thorough overview of the many ethical and social issues raised by computer technology with particular attention to artificial intelligence and its multifarious impact on society and human existence the purpose of this course is to offer a thorough overview of the most debated issues in the fields generally known as ethics of information technologies and ai ethics material in this folder you can find amplifying bias automating racism ethically framing the issue of algorithmic discrimination https github com dilettagoglia ai social ethical issues blob master diletta goglia algorithmic bias pdf a scientific paper research article final project for the exam the ai in justice league https github com dilettagoglia ai social ethical issues blob master diletta 20goglia 20 20eot 20presentation pdf a brief presentation about algorithmic bias event programme https github com dilettagoglia ai social ethical issues blob master talks pdf may 6th 7th 2020 list of partecipants and order of presentations lecture notes https github com dilettagoglia ai social ethical issues blob master s 26ei lecture notes goglia pdf my notes for this course further informations final grade 30 30 course topics information technologies purposeful behavior and intelligence singularity and superintelligence artificial agency free will consciousness artificial agents and responsibility machine ethics ai ethics and roboethics machine learning big data and bias anthropomorphism human computer robot interaction hci hri and human dignity automation ai and labour automation ai and social equity practical cases sex robots military robots self driving cars expert systems compas watson anthropomorphic emotional social robots hanson s sophia mit s kismet microsoft tay s twitter misadventure machine artistic creativity thenextrembrandt obvious art shimon | ai-ethics algorithmic-bias correlated-features bias ethical-artificial-intelligence | server |
min-LLM | min llm minimal code to train a relatively large language model 1 10b parameters minimal codebase to learn and adapt for your own use cases concise demonstration of tricks to optimally train a larger language model allows exploration of compute optimal models at smaller sizes based on realistic scaling laws the project was inspired by megatron https github com nvidia megatron lm and all sub variants this repo can be seen as a condensed variant where some of the very large scaling tricks are stripped out for the sake of readability simplicity for example the library does not include tensor parallelism pipeline parallelism if you need to reach those 100b parameter models i suggest looking at megatron https github com nvidia megatron lm setup make sure you re installing running on a cuda supported machine to improve performance we use a few fused kernel layers from apex if you re unsure what fused kernels are for i highly suggest this https horace io brrr intro html blogpost git clone https github com nvidia apex cd apex pip install v disable pip version check no cache dir global option cpp ext global option cuda ext install the rest of the requirements pip install r requirements txt train to train a 1 5b parameter model based on the megatron architecture sizes using 8 gpus model will not fit on 1 gpu with optimal throughput we scale to multiple deepspeed num gpus 8 train py batch size per gpu 16 references code mingpt https github com karpathy mingpt a lot of the base code was borrowed and extended from this awesome library microgpt https github com facebookresearch xformers blob main examples microgpt py a helpful example with xformers megatron deepspeed https github com microsoft megatron deepspeed learning the use of deepspeed with the megatron architecture 3d parallelism papers efficient large scale language model training on gpu clusters using megatron lm https cs stanford edu matei papers 2021 sc megatron lm pdf training compute optimal large language models https arxiv org pdf 2203 15556 pdf what language model to train if you have one million gpu hours https openreview net pdf id ri7bl3fhizq | ai |
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ESP8266-Weather-Station | gpio3 20s smartconfig press the button for 20 seconds to enter smartconfig mode image text https raw githubusercontent com hxy513696765 esp8266 weather station master pdf 20and 20schematic 20diagram schematic 20diagram bmp image text https github com hxy513696765 esp8266 weather station blob master photo weather 20display jpg raw true image text https github com hxy513696765 esp8266 weather station blob master photo weather 20data jpg raw true image text https github com hxy513696765 esp8266 weather station blob master photo hardware jpg raw true image text https github com hxy513696765 esp8266 weather station blob master photo smartconfig jpg raw true image text https raw githubusercontent com hxy513696765 esp8266 weather station master photo time png esp8266 rtos sdk esp8266 sdk based on freertos note apis of esp8266 rtos sdk are same as esp8266 nonos sdk more details in wiki about user rf cal sector set use this function to set the target flash sector to store rf cal parameters the user rf cal sector set has to be added in application but need not to be called it will be called inside the sdk the system parameter area 4 flash sectors has already been used so the rf cal parameters will be stored in the target sector set by user rf cal sector set since we do not know which sector is available in user data area users need to set an available sector in the user rf cal sector set for the sdk to store rf cal parameter if the user rf cal sector set is not added in the application the compilation will fail in link stage for example refer to user rf cal sector set in sdk examples project template user user main c note 1 esp init data bin has to be downloaded into flash at least once 2 download blank bin to initialize the sector stored rf cal parameter set by user rf cal sector set and download esp init data bin into flash when the system needs to be initialized or rf needs to be calibrated again requrements you can use both xcc and gcc to compile your project gcc is recommended for gcc please refer to esp open sdk https github com pfalcon esp open sdk compile clone esp8266 rtos sdk e g to esp8266 rtos sdk git clone https github com espressif esp8266 rtos sdk git modify gen misc sh or gen misc bat for linux export sdk path esp8266 rtos sdk export bin path esp8266 bin for windows set sdk path c esp8266 rtos sdk set bin path c esp8266 bin esp8266 rtos sdk examples project template is a project template you can copy this to anywhere e g to workspace project template generate bin for linux gen misc sh for windows gen misc bat just follow the tips and steps download eagle app v6 flash bin downloads to flash 0x00000 eagle app v6 irom0text bin downloads to flash 0x20000 blank bin downloads to flash 0x7e000 esp8266 weather station use esp8266 rtos sdk 1 4 x cjson resolution weather web json data and display on the oled12864 the code include esp8266 smartconfig function can use smartphone app connect to wifi d023fd73a58386feae7586fbd90474fcff2bc90f | os |
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design-system | california design system readme about the california design system status this project is currently in early beta status it s used in production on a few sites while we learn and continuously improve part of our early beta includes learning and collaborating with teams as the project matures we ll share more details on how we might meld the design system components into the state template why a design system we built this to address common needs identified by california s office of digital innovation odi and the california department of technology cdt during development of a href https alpha ca gov alpha ca gov a a href https covid19 ca gov covid19 ca gov a a href https cannabis ca gov cannabis ca gov a and a href https drought ca gov drought ca gov a how we work we start by understanding the needs of residents of california through user research we use an iterative design and development process to provide services that meet these needs as we work we continue to test our principles designs and components we prioritize equity to some degree by ensuring that our code is accessible and performant for all devices and bandwidths components a href https designsystem webstandards ca gov components pass through stages a of alpha beta and production as they become available to the design system we work in the open all our code and issue tracking is public we re happy to review pull requests from the public and state employees anyone can file an issue with bug reports and feature requests by selecting the new issue button from our a href https github com cagov design system issues issues page a you can see the prioritized list of issues we re addressing on our a href https github com orgs cagov projects 7 project board a be a part of the design system we re looking for partners to share feedback and improve the design system reach out to us through the a href https designsystem webstandards ca gov contact form a if you d like to work with us or participate through this github repository development instructions this project is a collection of components this means the individual widgets are easy to iterate on in isolation upgrade individually or exclude from a project to run this project locally checkout this repo and run these commands npm install npm run dev this will start up a local server running the site you see at a href https designsystem webstandards ca gov designsystem webstandards ca gov a you can find the components in the components directory the readme for each component is used to create the pages in the a href https designsystem webstandards ca gov components components section a interactive versions of each component are included in these pages each component is published to npm the following steps are run in a pre publish hook in the package json script new versions will not publish unless these steps complete the sample markup in the readme is matched to the sample markup used in the automated test any build steps like javascript concatenation or sass compilation are run and resulting files are stored in the dist folder automated tests are run including an axe accessibility checker the latest version of the component is written to our cdn so it can be included with a script tag we welcome your component modification requests or suggestions create a pull request or open an issue in this repository end to end testing playwright is used to run end to end and accessibility tests against local site urls using axe this set of tests is configured to be run against any open pr in our git actions a href tree main github workflows validate yml validate yml a they can be run locally with npm run test site if you run into test failures due to an axe detected accessibility violation the offending node will be part of the test failure output you can get more information on this type of failure by using the axe chrome plugin and letting it evaluate the page locally during development the playwright test setup uses http server instead of 11ty because axe playwright flags the browser sync element that is injected into 11ty s default local serve mode as an accessibility violation for not being included in a landmark element unit tests each component contains its own unit tests the tests for all components are run in sequence when any commit is made in this repo via a husky pre commit hook when a package is published its own unit tests are run via an npm prepublish hook configured in the component s package json you can run any components tests individually during development with the npm test command inside its directory these component unit tests were created following a href https open wc org open web components a recommendations directory structure the code for all the components is in components each directory contains the entire package for each component published to npm the markdown files in each component s directory provide the content used in the component gallery section of the a href https designsystem webstandards ca gov design system site a any modification to a component s code or documentation should be associated with a new version of the component being published publishing a new version of a component to npm will run the scripts that publish its code to the ca cdn more information on component development in the a href tree main components readme component readme a the rest of the site content is in docs docs pages markdown for additional pages docs site templates for site layouts docs src css sass index scss site css dependencies docs src js index js site js dependencies | os |
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Bitcoin-Simulator | bitcoin simulator capable of simulating any re parametrization of bitcoin bitcoin simulator is built on ns3 the popular discrete event simulator we also made use of rapidjson to facilitate the communication process among the nodes the purpose of this project is to study how consensus parameteres network characteristics and protocol modifications affect the scalability security and efficiency of proof of work powered blockchains our goal is to make the simulator as realistic as possible so we collected real network statistics and incorporated them in the simulator specifically we crawled popular explorers like blockchain info to estimate the block generation and block size distribution and used the bitcoin crawler to find out the average number of nodes in the network and their geographic distribution futhermore we used the data provided by coinscope regarding the connectivity of nodes we provide you with a detailed installation guide http arthurgervais github io bitcoin simulator installation html containing a video tutorial http arthurgervais github io bitcoin simulator installation html to help you get started you can also check our experimental results http arthurgervais github io bitcoin simulator results html and our code feel free to contact us or any questions you may have crafted through research the bitcoin simulator was developed as part of the following publication in ccs 16 on the security and performance of proof of work blockchains https eprint iacr org 2016 555 pdf latex inproceedings gervais2016security title on the security and performance of proof of work blockchains author gervais arthur and karame ghassan and w st karl and glykantzis vasileios and ritzdorf hubert and capkun srdjan booktitle proceedings of the 23nd acm sigsac conference on computer and communication security ccs year 2016 organization acm | blockchain |
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starling | starling starling is a graphical tool to build simple computer vision applications developped by the imagine http liris cnrs fr imagine r3am http liris cnrs fr r3am index en html and saara http liris cnrs fr saara doku php teams of the liris laboratory http liris cnrs fr starling provides an easy to use graphical environment to experiment computer vision and to generate c code examples of vision algorithms use it relies on the opencv library www opencv org gui https github com liris vision starling blob master doc user guide gui png it s a fork of the harpia project http s2i das ufsc br harpia en home html main improvements from harpia 1 1 are generates c code using opencv 2 x c api makes new module integration easier by adding just one xml file replaces some components by new standards xml parser canvas it can also be used for opencv experimenting and learning starling runs on linux macos and windows a debian package for ubuntu is available here http liris cnrs fr liris vision | ai |
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UCF-09-SQL-EMPLOYEE-DATABASE | sql employee database a mystery in two parts background sql was used to analayze the data for employees of a corporation from the 1980s and 1990s the database of employees from that period consists of six csv files both data engineering and analysis were conducted tables were designed to hold data in the csvs data were imported into a sql database and queries were written and performed to answer questions about the data in a second part of this project the sql database was imported into jupyter notebook so that the data could be further visualized using pandas plots from pandas suggest that the original date may be fake | server |
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libcvd | libcvd note the master branch is now libcvd 2 0 which is in beta and requires c 14 compiling and installing libcvd currently has both an autoconf and cmake based build system the autoconf one works on any unix like system and is well testes the cmake one should work on any system but is a little newer so may be buggy in untested configurations to install on a unix system configure make sudo make install to verify that a few things work you can optionally run make test to build on unix with cmake https cmake org mkdir build cd build cmake dcmake build type release make and optionally make test to build on windows use cmake https cmake org mkdir build cd build cmake dcmake install prefix directory cmake build target install config release dependencies there are no mandatory dependencies for a reasonably complete installation you probably want toon header only install from source libjpeg libtiff libpng ffmpeg x11 opengl you might also want libdc1394 libuvc https github com ktossell libuvc on ubuntu 16 04 run sudo apt get install libjpeg dev libpng dev libtiff dev libx11 dev libavformat dev libavdevice dev libavcodec dev libavutil dev libswresample dev libglu dev libdc1394 22 dev system compatibility you need a c 14 compiler all libraries are optional but you will be missing features if the libraries aren t present the configure script will tell you what s present and what s not ubuntu 16 04 gcc 5 the default c compiler on ubuntu 16 04 will not compile libcvd because of a bug in the standards compliance of the compiler https stackoverflow com questions 34280729 throw in constexpr function if you want to use libcvd you will need to install a newer compiler the easiest eay is to add the toolchain test ppa https launchpad net ubuntu toolchain r archive ubuntu test sudo add apt repository ppa ubuntu toolchain r test sudo apt get update sudo apt get install g 7 now you can build libcvd with either cxx g 7 configure make or mkdir build cd build cxx g 7 cmake dcmake build type release make documentation documentation status https codedocs xyz edrosten libcvd svg https codedocs xyz edrosten libcvd latest documentation here https codedocs xyz edrosten libcvd or just run doxygen status of unit tests build status https circleci com gh edrosten libcvd svg style shield circle token db58907af52b26d11f2c4f5de2ff3b1a59543ddc news and main page https www edwardrosten com cvd | ai |
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tech | tech information technologies | server |
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lyrics-exploration-green-day | lyrics exploration green day about green day is a pop punk american band as a big fan of their work and as a data scientist i wanted to test some of nlp techniques with their lyrics | ai |
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To-Do-PoC | to do app proof of concepts note this project is no longer in active development this was a semester project as a proof of concept intending to use multiple components rather that a framework which is a not a good idea thus making this more of a proof of bad concepts and thus cannot be developed into something which is better that other products on the market python style check https github com enygmator to do poc workflows python 20style 20check badge svg this app will help you to stay on top of your daily tasks it shows you what you have to do and at the same time classifies the tasks for you to manage your responsibilities work or just your wish to do something this app was created not to be used in production but far from it it was made to show a proof of concept which was to achieve the following make a webserver to run the app using only http server and no other web framework to learn concepts related to webservers upload the python code as a package to pypi org to learn python packaging use azure devops now migrated to github and learn team collaboration using devops methods use github for open sourcing the project and learn concepts related to workflows on github using git as a vcs version control system to effectively manage the repository s history and the development of the app to use sqlite3 on the backend to learn dbms implement various features of python in a single project implement a model similar to the mvc model view controller architecture learn web development we used html css and extensive javascript note you will notice that a lot of code is unnecessarily lengthy redundant and abnormal as compared to normal coding practices the reason for most of these situations is that this project was made as part of a 1st semester cse engg project and thus had to incorporate various features and explore situations in code which are not normal that is try out various possibilities so this project does not aim to write good code rather it intends to show a proof of concept where multiple components come together to make what could actually be replaced by a single web framework more information to get to know more details of the project check out the wiki which has documented the code pretty well with a lot of explanation you may also open issues to intimate of any errors problems warnings or just to get in touch with the developer installation build note that you must have pip3 and python3 installed direct installation from pypi this app can be installed directly from the pypi repo by just running the command pip3 install todopoc in a cli cloning the repo and installing it you can clone the repo and open the cli with the path set to the main project folder containing run ps1 and run sh now in linux wsl run run sh in bash and or in windows run run ps1 in powershell as admin choose the appropriate options and you are done installing it install from release you can download the zip file from the release section and install it from there by executing pip install todopoc zip not working at the moment usage after installing the package you can run the app from any python interactive terminal or code file like this py import todopoc as app app start this starts the app the server and port number are mentioned as the output by default it is set to http localhost 8182 you can use ctrl c to stop the app development if you want to develop or debug it you need to clone the source code and install it using the above instruction and make sure you choose y when asked if you want to install it in development mode while developing it you can use the app usual the way it is mentioned in usage usage except for the fact that you will have to import it every single time after making changes a simpler method would be to run the webserver py file directly using python but beware you will have to temporarily change some import statements in files such as mastercontrol py license this project has been open sourced on github with the apache license version 2 0 january 2004 read the license file for further details contributions this project has no intention of being developed but developers are free to use this code for their own purposes according to the license if you want to contribute then you can open a pr and i will gladly look into it feel free to contact the developers project details created by group 10 1 moyank giri 2 vaibhava krishna d 3 t tarun aditya lead developer and project manager a python project semester 1 of batch 2019 23 ue19cs102 introduction to computing using python lab section m pes university bengaluru karnataka india | todo todolist python pythonserver html-css-javascript sqlite3 python-sqlite3 mvc-framework mvc-architecture http-server | server |
laravel-sketchpad | laravel sketchpad introduction sketchpad is an innovative bolt on development environment for your existing laravel site it s a place to write test experiment and execute code or just a place to group useful tools and functions you want easy access to image https cloud githubusercontent com assets 132681 24509000 14ee5b96 155d 11e7 95ed 7712a32dceb6 png overview sketchpad provides an alternative structure of controllers views assets and routing that lives alongside your main laravel installation you can navigate methods interactively modify parameters and even live reload https github com davestewart laravel sketchpad reload the code you re working on all from a friendly ui additional tooling and functionality is designed to make it quick and easy to develop or debug new tools features and code demo view the live demo at sketchpad davestewart io sketchpad http sketchpad davestewart io sketchpad uses test out new coding ideas develop tools and utilities for everyday work debug applications in the same environment they re running in practice with new libraries or apis with your existing code give new hires a place to get up to speed with the codebase double check how that rarely used function actually works give all that secret or commented out code somewhere to live live document existing features and tools generate and manage reports more info for installation and usage instructions see the wiki https github com davestewart laravel sketchpad wiki | laravel testing tinker live-coding demo prototyping tooling | front_end |
design-system | j design system jds thumb https github com react95 io react95 assets 46988995 ac97b7f7 2420 40f6 9990 25217a8381df introduction j design system ui react design system ui library why you should use j design system compound component as html role cjs esm uncontrolled component form react hook form https react hook form com project overview storybook link https designsystemlab github io design system yarn berry yarn berry https github com designsystemlab design system wiki package manager yarn berry compound component https github com designsystemlab design system wiki compound component pattern monorepo architecture https github com designsystemlab design system wiki monorepo architecture keyboard control https github com designsystemlab design system wiki keyboard control dependency cruiser https github com designsystemlab design system wiki dependency cruiser emotion https github com designsystemlab design system wiki styling library emotion jest react testing library https github com designsystemlab design system wiki test using jest react testing library tsup https github com designsystemlab design system wiki tsup ec 9d 84 ed 99 9c ec 9a a9 ed 95 9c eb b2 88 eb 93 a4 eb a7 81 changeset ci cd https github com designsystemlab design system wiki f0 9f a6 8b changeset ci cd installation emotion emotion dpendency react ui component bash npm install save jdesignlab react emotion react 11 svg icons bash npm install save jdesignlab react icons emotion react 11 ui import readme readme jsx import text textinput button modal from jdesignlab react import mail bell book from jdesignlab react icons components box img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages box img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs layout box basic button img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages button img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs actions button basic card img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages card img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs data display card basic checkbox img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages checkbox img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs data display card basic drawer img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages drawer img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs actions drawer basic dropdown img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages dropdown img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs layout dropdown basic flex img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages flex img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs layout flex basic input img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages input img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs forms textinput basic modal img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages modal img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs actions modal basic popover img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages popover img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs actions popover basic radio img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages radio img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs forms radio basic select img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages select img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs forms select basic stack img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages stack img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs layout stack basic tabs img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages tabs img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs navigation tabs basic text img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages tabs img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs typography text basic textarea img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages textarea img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs forms textarea basic tooltip img src https img shields io badge readme 1dd1a1 style flat logo readme logocolor white https github com designsystemlab design system tree main packages tooltip img src https img shields io badge storybook ff4785 style flat logo storybook logocolor white style https designsystemlab github io design system path docs layout stack basic contributors yondo123 https github com yondo123 br yoycode https github com yoycode br img src https avatars githubusercontent com u 46988995 v 4 alt tooooo1 width 64 height 64 img src https avatars githubusercontent com u 65293082 v 4 alt helen width 64 height 64 convention commit convention rule description feat style remove structure modify fix refactor doc chore package version configure deploy test ui | design-system storybook styleguide react css-in-js emotion typescript | os |
ZocSec.SecurityAsCode.AWS | zocsec securityascode aws p img src zocsecshieldblue png align right welcome to the zocdoc information security team zocsec securityascode repository for aws part of the zocsec mandate is to use aws s in built technologies to automate the remediation of common security problems in this repository zocsec presents code configuration used to lock down our aws environment p project list these are the projects we re currently ready share s3 bucket automated encryption s3 auto encrypt this aws module will automatically encrypt s3 buckets that become unencrypted ec2 malware automated termination ec2 auto terminate an automated means to euthanize ec2 instances which have been compromised by malware security group automated update security group auto update a mechanism to automatically correct security groups that expose sensitive ports to 0 0 0 0 0 we will be sharing more of our projects in the future contributions we welcome contributions and pull requests to this repo give us feedback the primary contributors to this effort are jay ball veggiespam https github com veggiespam and gary tsai garymalaysia https github com garymalaysia copyright 2018 zocdoc inc www zocdoc com vim spell noexpandtab sw 4 sts 4 ts 4 | criticality-1 team-infosec open-source add-me infrastructure | server |
mobile_app_dev | mobile app dev projects for my mobile application development course at neu includes a basic version of boggle and a multiplayer version of boggle i reserve no rights to these projects as boggle is a trademarked game i received an a in this course | front_end |
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go-wit | go wit wercker status https app wercker com status db550f8016d3e02bd1d79d94bccf764b wercker status https app wercker com project bykey db550f8016d3e02bd1d79d94bccf764b a go library for the wit ai http wit ai api for natural language processing version 0 3 installation go get github com jsgoecke go wit documentation http godoc org github com jsgoecke go wit http godoc org github com jsgoecke go wit usage go package main import github com jsgoecke go wit encoding json log os func main client wit newclient os getenv wit access token process a text message request wit messagerequest request query hello world result err client message request if err nil println err os exit 1 log println result data json marshalindent result log println string data process an audio wav message request wit messagerequest request file audio sample helloworld wav request contenttype audio wav rate 8000 result err client audiomessage request if err nil println err os exit 1 log println result data json marshalindent result log println string data output structs bf699a8f bc90 4fb4 a715 bd8bd77749db hello world hello 0 996 54ed4e6d 0653 453e 8c0c 81da57c3846c hello world hello 0 993 json msg id 76f1c370 bd92 417f 8cb3 e1419d1a9cb3 msg body hello world outcome intent hello entities metric datetime null confidence 0 996 msg id 322f9b61 0f75 4953 a392 f8eca058a12f msg body hello world outcome intent hello entities metric datetime null confidence 0 993 testing must have the environment variable wit access token set to your wit api token cd go wit go test test coverage http gocover io github com jsgoecke go wit http gocover io github com jsgoecke go wit lint http go lint appspot com github com jsgoecke go wit http go lint appspot com github com jsgoecke go wit license mit see license txt author jason goecke jsgoecke http twitter com jsgoecke | ai |
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Errow | errow an arcade mobile game using the corona sdk https coronalabs com this game is in development errow is an arcade game where you are in a dungeon and need to protect yourself against arrow traps you can also train in the city with the infinite mode before adventuring in the dungeon screenshot https i imgur com fncitij png sprites by game pixel boy https twitter com 2pblog1 sparklin labs https github com sparklinlabs superpowers asset packs visit their site http superpowers html5 com icons kenney https kenney nl font by kenney https kenney nl using kenney fonts https kenney nl assets kenney fonts | game-development game coronasdk | front_end |
ECommerce-FoodApp- | ecomm food app a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https docs flutter dev get started codelab cookbook useful flutter samples https docs flutter dev cookbook for help getting started with flutter development view the online documentation https docs flutter dev which offers tutorials samples guidance on mobile development and a full api reference | server |
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UML-Parser-reverse-engineering-java | a tool that reverse java source code to uml class and sequence diagrams uml parser the uml parser is a web based application that receives java source codes as input analyzes the java source codes and finally produces uml class and sequence diagrams this application uses tomcat and mysql database i have created a simple database this application purpose was reverse engineering not a complex database d tools used 1 plantuml uml diagrams can be generated using simple and intuitive language used by plantuml http plantuml com 2 yuml create and share simple uml diagrams https yuml me 3 javaparser uml parser flow the below figure it represents the main flow and architecture of uml parser in the beginning the user enters the site then the system will prepare the ui to the user after that the user enters his username and password the system will perform the login process by taking the user input and map and compare it with the database if the login process succeeded uml parser menu will appear to the user at this point an input and output path will appear to the user in the uml parser menu then the user must put the java source code which needs to be reversed in the input path in addition the user has two option to choose either class diagram or sequence diagram the system will take that input information to prepare it and send it to uml parser engine uml parser engine will handle this request then start parsing and creating the needed diagram to send it to yuml if it is a class diagram or to create puml notation to draw a sequence diagram when the parsing is complete an image will be created according to the selected diagram furthermore uml parser engine will store the data as text and image in the database and generate a traceability report which the user can find it in output path given by the uml parser this image will appear to the user in the uml parser menu and saved to the output path nbproject flow png uml parser database please note that you have to install mysql database create the below tables create user manually in the data base and change the connection string which located src java db dbmanager java the below are the database tables images imageid int imagepath varchar 2500 imagetype varchar 5 sequence int imagebinary blob date datetime createdbyuserid int releaseversion varchar 5 users userid int username varchar 15 password varchar 20 usertypeid int userstatusid int lastlogintime datetime userstatus userstatusid int userstatusdescription varchar 45 usertype usertypeid int usertypedescription varchar 45 imagetype imagetypeid int imagetypedescription varchar 45 user interface of uml parser nbproject login png put the java source code in the input path that the website gave you nbproject umlparsermenu png nbproject umlparseradminconsole png acknowledgment this work was done for my master s degree in jordan university jordan amman it took me a lot of time and effort to finish this application alone now i m sharing my work to you hoping this might be helpful ala almarayat | server |
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syllabus-EDM5240-H2019 | edm5240 technologies de l information appliqu es au journalisme ou journalisme informatique session d hiver 2019 ce syllabus est accessible en ligne partir de l url jhroy gitbooks io edm5240 h2019 https jhroy gitbooks io edm5240 h2019 p dagogie transparente nbsp pour faire le suivi des changements apport s ce syllabus depuis sa mise en ligne initiale consultez la page de ses commits https github com jhroy syllabus edm5240 h2019 commits master certains l ments de ce document dans ses versions en ligne ou pdf sont en anglais veuillez m en excuser je n ai pas trouv la fa on de les franciser sur gitbook https www gitbook com les sources documentaires des journalistes sont aujourd hui pour l essentiel des bases de donn es les gouvernements les entreprises les m dias les individus par leurs interactions dans les m dias sociaux g n rent des quantit s vertigineuses de donn es ces donn es sont souvent accessibles de diff rentes fa ons c est ainsi que d y puiser est de plus en plus consid r comme une comp tence fondamentale du m tier d aucuns affirment que les nbsp news getters nbsp de demain sont les journalistes qui seront capables de harnacher ces bases de donn es puis d y trouver et de raconter les histoires qu elles rec lent ce cours va vous y aider en vous montrant quelques unes des technologies de l information qui peuvent tre appliqu es au journalisme sans ne jamais perdre de vue ses principes fondamentaux http www gallimard fr catalogue gallimard folio folio actuel principes du journalisme br il aurait pu s intituler nbsp datajournalisme nbsp ou nbsp journalisme de donn es nbsp mais nous craignions que cette appellation vieillisse mal un peu comme nbsp journalisme assist par ordinateur nbsp qui fait tr s 1990 le contenu de ce cours en fait colle davantage ce qu on appelle dans le monde anglo saxon du computational journalism c est l application de l informatique au journalisme ce syllabus m me est un exemple de ce qu on peut faire avec ces technologies il repose sur un syst me appel git https fr wikipedia org wiki git qui permet de collaborer plusieurs sur un m me projet c est l une des technologies que l on appr hendera au cours de la session le cours a lieu tous les mercredis entre 14h et 17h au local j 4315 du 9 janvier au 17 avril 2019 br avoir son propre ordinateur est essentiel pour suivre ce cours assets logouqam png | journalisme journalisme-de-donnees data-journalism computational-journalism | server |
mqttclient | https img shields io github v tag jiejietop mqttclient color brightgreen label version https github com jiejietop mqttclient releases license https img shields io badge license apache blue svg https github com jiejietop mqttclient blob master license https img shields io github forks jiejietop mqttclient https img shields io github stars jiejietop mqttclient https img shields io badge platform linux windows mac embedded orange svg readme cn md mqttclient a high performance high stability cross platform mqtt client a high performance high stability cross platform mqtt client developed based on the socket api can be used on embedded devices freertos liteos rt thread tencentos tiny linux windows mac and has a very concise the api interface realizes the quality of service of qos2 with very few resources and seamlessly connects the mbedtls encryption library advantage extremely high stability whether it is drop and reconnect packet loss and retransmission it is strictly abide by the mqtt protocol standard in addition to the test of large data volume is very stable whether it is receiving or sending and the high frequency test is also very stable lightweight the entire code project is extremely simple without mbedtls it takes up very few resources the author used the esp8266 module to communicate with the cloud the entire project code consumes less than 15k of ram support mbedtls encrypted transmission make the network transmission more secure and the interface layer does not require users to care no matter whether it is encrypted or not mqttclient is fixed for the api interface provided by the user which is very compatible a set of codes on behalf of the application layer can be transmitted with or without encryption supports multiple clients compatible with multiple clients running at the same time one device connected to multiple servers supports synchronous and asynchronous processing applications need not block and wait to waste cpu resources support interceptor configuration on some platforms the client will automatically subscribe to the system theme by default and the theme from the server changes every time in this case you need to use an interceptor to intercept and separate the theme and data information and deliver it to users greatly improving flexibility with online code generation tool the code can be generated with extremely simple configuration address https jiejietop gitee io mqtt index html https jiejietop gitee io mqtt index html has a very simple api interface in general mqttclient configuration has default values basically can be used without configuration can also be arbitrarily configured the configuration has robustness detection so designed the api interface is also very simple multifunctional parameters can be configured and tailored reconnect time interval heartbeat period maximum number of subscriptions command timeout read and write buffer size interceptor processing etc parameters can be tailored and configurable to meet the needs of developers complex and simple to use in various development environments support automatic re subscription of topics after automatic reconnection to ensure that the topics will not be lost support theme wildcards subscribed topics are completely separated from message processing making programming logic easier and easier to use users don t have to deal with intricate logical relationships the keep alive processing mechanism has been implemented in mqttclient without the user having to deal with the psychological experience the user only needs to concentrate on the application function has a very good design designed the recording mechanism with very few resources and retransmits the message when it is lost to ensure that the qos1 and qos2 service quality levels guarantee its service quality there are very good code styles and ideas the whole code adopts a layered design and the code implementation adopts the idea of asynchronous processing to reduce coupling and improve performance developed on top of standard bsd socket as long as it is compatible with bsd socket system seamless connection of salof it is a synchronous and asynchronous log output framework it outputs the corresponding log information when it is idle and it can also write the information into flash to save it which is convenient for debugging use the famous paho mqtt library package no other dependencies online code generation tool this project has a code generation tool that only requires online configuration to generate code which is extremely simple and easy to use the code generation tool address is https jiejietop gitee io mqtt index html https jiejietop gitee io mqtt index html online code generation tool png mqtt tool png occupied resource size a total of 10857 bytes of rom and the overhead of ram is almost only dependent on dynamic memory without using tls encrypted transmission the communication dynamic memory that maintains the qos0 quality of service level requires only about 3694 bytes including 1024 read buffer 1024 write buffer 1024 internal thread stack size compared with other mqtt clients mqttclient requires very little ram resource overhead code ro data rw data zi data object name 7118 791 0 0 mqttclient o 546 0 0 0 mqttconnectclient o 212 0 0 0 mqttdeserializepublish o 476 0 4 0 mqttpacket o 236 0 0 0 mqttserializepublish o 310 0 0 0 mqttsubscribeclient o 38 0 0 0 mqttunsubscribeclient o 56 0 0 0 nettype tcp o 62 0 0 0 network o 24 0 0 0 platform memory o 40 0 0 0 platform mutex o 344 0 0 0 platform net socket o 94 0 0 0 platform thread o 70 0 0 0 platform timer o 246 0 4 0 random o 62 0 0 0 mqtt list o 10066 791 8 0 total overall framework has a very clear layered framework overall frame png mqttclient png at the top of the framework is the api function interface which implements the client s application release set parameters connect to the server disconnect subscribe topic unsubscribe topic publish message and other functional interfaces the famous paho mqtt library is used as the mqtt message packet library asynchronous processing mechanism is used to manage all the acks it does not need to wait for the server s response when sending the message but only records it after receiving the server s ack cancel this record very efficient and when the mqtt message qos1 qos2 is sent and no response is received from the server the message will be retransmitted an mqtt yield thread is implemented internally to handle all content in a unified manner such as timeout processing ack message processing and receiving publish message from the server at this time the callback function will be called inform the user of the data received post release post completion message processing heartbeat message keep alive when disconnected from the server you need to try to reconnect resubscribe to the topic resend the message or reply wait message processing such as reading and writing messages decoding mqtt messages setting messages dup flag destroying messages and other operations network is a network component which can automatically select a data channel if it is an encryption method tls encryption is used for data transmission and tls can choose mbedtls as the encryption backend it can also be the tcp direct connection method is ultimately transmitted via tcp platform is a platform abstraction layer that encapsulates things from different systems such as socke or at thread time mutex memory management these are dealing with the system and are also necessary for cross platform package on the far right is the general content list processing log library error handling software random number generator etc supported platforms at present linux tencentos tiny freertos rt thread platforms have been implemented software package is named kawaii mqtt in addition to tencentos tiny at framework can also be used and the stability is excellent platform code location linux https github com jiejietop mqttclient https github com jiejietop mqttclient tencentos tiny https github com tencent tencentos tiny tree master board fire stm32f429 https github com tencent tencentos tiny tree master board fire stm32f429 tencentos tiny at framework https github com jiejietop gokit3 board mqttclient https github com jiejietop gokit3 board mqttclient rt thread https github com jiejietop kawaii mqtt https github com jiejietop kawaii mqtt freertos https github com jiejietop freertos mqttclient https github com jiejietop freertos mqttclient version release version description v1 0 0 initial release complete basic framework and stability verification v1 0 1 fix the logical processing when actively disconnecting from the server v1 0 2 add a new feature interceptor fix some small bugs v1 0 3 to avoid global pollution modify the naming of log and list related functions v1 0 4 network structure and mbedtls data channel readjusted v1 1 0 a larger version of the update refactoring part of the code optimizing the logic of mqtt processing improving the overall stability supporting multiple clients supporting setting the will optimizing the api interface and adding multiple cloud platforms test code and documentation add online code generation tool online cutting configuration tool question welcome to submit issues and bug reports in the form of github issues https github com jiejietop mqttclient issues copyright and license mqttclient follows the apache license v2 0 https github com jiejietop mqttclient blob master license open source agreement encourage code sharing and respect the copyright of the original author you can freely use and modify the source code or you can publish the modified code as open source or closed source software test and use under linux platform install cmake bash sudo apt get install cmake g test program test platform location emqx my privately deployed server test emqx test c test emqx test c baidu tiangong test baidu test c test baidu test c onenet test onenet test c test onenet test c alibaba cloud internet of things test ali test c test ali test c compile run bash build sh after running the build sh script the executable files emqx baidu onenet and other platforms will be generated under the build bin directory executable programs can be run directly bash build bin emqx compile into a dynamic library libmqttclient so bash make libmqttclient sh after running the make libmqttclient sh script a dynamic library file libmqttclient so will be generated in the libmqttclient lib directory and installed into the system s usr lib directory the relevant header files have been copy to the libmqttclient include directory and copy it to your project you only need to link the dynamic library when compiling the application lmqttclient lpthread the configuration file of the dynamic library is based on test mqtt config h configuration if you are using a cross compiler you should export the corresponding environment variables according to the compiler you are using the cross compiler used here is arm linux gnueabihf gcc and you must also set the dynamic library file libmqttclient so is copied to the usr lib directory of your embedded system bash export cross compile arm linux gnueabihf if you need to uninstall libmqttclient so execute the following command bash make libmqttclient sh remove learn more please see the documentation mqtt protocol introduction docs mqtt introduction md mqtt protocol communication process docs mqtt communication md mqttclient code generation tool docs mqtt tool md mqttclient configuration and cutting tool docs mqtt config md mqttclient design and implementation docs mqtt design md mqttclient connects to baidu tiangongwu access docs mqtt baidu md mqttclient connects to onenet cloud platform docs mqtt onenet md mqttclient connects to alibaba cloud iot platform docs mqtt aliyun md | tcpip mqtt mqtt-client network linux rtos mbedtls ssl cross-platform socket-mqtt qos1 mqttclient qos2 net | os |
souliss | souliss souliss is a networking framework for interconnected things smart homes and automated appliances it includes a network layer that gives virtualization over the communication media an event based protocol and datastructure and user interfaces based on android and openhab this repository contains the code that runs over arduino avr and esp8266 and the examples to getting started getting started if you are new to souliss and arduino the getting started guide https github com souliss souliss wiki getting 20started 20with 20souliss gives the basic to compile and run souliss on your nodes supported hardware you can run souliss on consumer products https github com souliss souliss wiki product or compatible boards https github com souliss souliss wiki supported 20hardware 20platform generally speaking any device based on the supported microcontrollers and transceivers https github com souliss souliss wiki supported 20hardware can run souliss download stable code is released periodically details are available in the download page https github com souliss souliss wiki downloads support and community the main source for documentation is the wiki https github com souliss souliss wiki notify bugs in the issues https github com souliss souliss issues and get in touch with the community https github com souliss souliss wiki community | server |
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ingest-data-to-postgres | ingest data to postgres this is a data engineering etl project that involves writing a script that pulls data from the internet in csv gz format merge the data from diverse files into one by reading from each file using pandas library then read from the merged file transform this new data and then load the data into postgresql database | server |
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xchataqua | build status https travis ci org xchataqua xchataqua svg branch master https travis ci org xchataqua xchataqua x chat aqua x chat aqua is a xchat front end for mac os x visit us http xchataqua github io http xchataqua github io moved from http sourceforge net projects xchataqua http sourceforge net projects xchataqua xchat azure xchat azure is a new brand of x chat aqua especially for apple appstore see below for details downloads 10 7 10 8 official appstore release is working on latest 2 versions of os x download it from appstore http itunes apple com app id447521961 for older os x versions or development version visit http xchataqua github io download http xchataqua github io download i lost all configurations after update to 1 11 or later your configuration has gone your configuration is not saved when you quit the application auto recovery script 0 warning do not run this script while running xchat azure 1 download the script http xchataqua github com downloads fixdata tar 2 run the script it will show the result no bad message means good result for profesional after azure 1 11 it is using app sandboxing by mac appstore policy in most of case this is occured because your azure configuration directory is symlink to other one to recover this find the original configuration and replace symlink to original one 1 quit azure 2 remove all the configurations from sandbox container rm rf library containers org 3rddev xchatazure 3 find your original configuration candidates are 1 xchat2 if you moved from ancient aqua or gtk 2 library application support x chat aqua if you moved from aqua 3 library application support xchat azure if something goes wrong with azure where is my config files where is my log files look in library containers org 3rddev xchatazure data library application support xchat azure where means your home directory for example users myname move gtk xchat2 or xchat aqua to azure note do not try this if you don t understand what you are doing if you are now using xchat gtk or x chat aqua and now you want to move to xchat azure you should do some work unlike xchat gtk and x chat aqua xchat azure do not share the traditional configuration direcotry xchat2 because of mac app store guideline so if you want to keep your configuration you should move it copy it or make hard link 1 quit azure 2 open terminal app to do this job you can find it on spotlight 3 did you run xchat azure already remove its configuration to do other job rm rf library containers org 3rddev xchatazure 4 move if you will not use xchat gtk or x chat aqua again for gtk mv xchat2 library application support xchat azure for aqua mv library application support x chat aqua library application support xchat azure 5 copy if you will use different configuration on aqua and azure or you want to test azure and get back to original x chat aqua for gtk cp r xchat2 library application support xchat azure for aqua cp r library application support x chat aqua library application support xchat azure why has x chat aqua been renamed mac app store is very useful place to distribute applications for non geek users most of debian ubuntu users do not want to search applications on broad internet world if you can access alternative or even the same applications on aptitude so mac os x users got the mac app store but i should keep mac app store guidelines to submit x chat aqua there were some issues main reason any application on mac app store may not use names that include mac os x aqua or any other of apples trademarks there was no way so i dropped the name also there was several other issues because i didn t want to change x chat aqua developement policy we should remove whole ppc ppc64 support we should support only os x 10 6 or later we should remove update module sparkle in this case we should not work on xchat2 the traditional configutaion directory it should be annoying job to keep these on x chat aqua | xchat xchat-aqua xchat-azure mac-appstore irc-client irc | front_end |
localfirstweb.dev | localfirstweb dev this is list of various resources for local first web development hosted at https localfirstweb dev please help keep this up to date and looking pretty pull requests are very welcome for review the content is https github com localfirstweb localfirstweb dev blob main data content json the design is https github com localfirstweb localfirstweb dev blob main assets css main scss if you want to run the site locally with jekyll https jekyllrb com check it out and then run jekyll serve or bundle exec jekyll serve at the top level of the repo to run with docker run docker run p 4000 4000 v pwd site bretfisher jekyll serve | front_end |
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Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original | ml for trading 2 sup nd sup edition this book https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d aims to show how ml can add value to algorithmic trading strategies in a practical yet comprehensive way it covers a broad range of ml techniques from linear regression to deep reinforcement learning and demonstrates how to build backtest and evaluate a trading strategy driven by model predictions in four parts with 23 chapters plus an appendix it covers on over 800 pages important aspects of data sourcing financial feature engineering and portfolio management the design and evaluation of long short strategies based on supervised and unsupervised ml algorithms how to extract tradeable signals from financial text data like sec filings earnings call transcripts or financial news using deep learning models like cnn and rnn with market and alternative data how to generate synthetic data with generative adversarial networks and training a trading agent using deep reinforcement learning p align center a href https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d img src https ml4t s3 amazonaws com assets cover toc gh png width 75 a p this repo contains over 150 notebooks that put the concepts algorithms and use cases discussed in the book into action they provide numerous examples that show how to work with and extract signals from market fundamental and alternative text and image data how to train and tune models that predict returns for different asset classes and investment horizons including how to replicate recently published research and how to design backtest and evaluate trading strategies we highly recommend to review the notebooks while reading the book they are usually in executed state and often contain additional information that the space constraints of the book did not permit to include what s new in the 2 sup nd sup edition first and foremost this book https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r vmkjpzc4n36ttzzcwatp pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 8f331266 0d21 4c76 a3eb d2e61d23bb31 pd rd w kvgnf pd rd wg lylkh ref pd gw ci mcx mr hp d demonstrates how you can extract signals from a diverse set of data sources and design trading strategies for different asset classes using a broad range of supervised unsupervised and reinforcement learning algorithms it also provides relevant mathematical and statistical knowledge to facilitate the tuning of an algorithm or the interpretation of the results furthermore it covers the financial background that will help you work with market and fundamental data extract informative features and manage the performance of a trading strategy from a practical standpoint the 2nd edition aims to equip you with the conceptual understanding and tools to develop your own ml based trading strategies to this end it frames ml as a critical element in a process rather than a standalone exercise introducing the end to end ml for trading workflow from data sourcing feature engineering and model optimization to strategy design and backtesting more specifically the ml4t workflow starts with generating ideas for a well defined investment universe collecting relevant data and extracting informative features it also involves designing tuning and evaluating ml models suited to the predictive task finally it requires developing trading strategies to act on the models predictive signals as well as simulating and evaluating their performance on historical data using a backtesting engine once you decide to execute an algorithmic strategy in a real market you will find yourself iterating over this workflow repeatedly to incorporate new information and a changing environment p align center img src https i imgur com kcgitgp png width 75 p the second edition https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d s emphasis on the ml4t workflow translates into a new chapter on strategy backtesting 08 ml4t workflow a new appendix 24 alpha factor library describing over 100 different alpha factors and many new practical applications we have also rewritten most of the existing content for clarity and readability the trading applications now use a broader range of data sources beyond daily us equity prices including international stocks and etfs it also demonstrates how to use ml for an intraday strategy with minute frequency equity data furthermore it extends the coverage of alternative data sources to include sec filings for sentiment analysis and return forecasts as well as satellite images to classify land use another innovation of the second edition is to replicate several trading applications recently published in top journals chapter 18 18 convolutional neural nets demonstrates how to apply convolutional neural networks to time series converted to image format for return predictions based on sezer and ozbahoglu https www researchgate net publication 324802031 algorithmic financial trading with deep convolutional neural networks time series to image conversion approach 2018 chapter 20 20 autoencoders for conditional risk factors shows how to extract risk factors conditioned on stock characteristics for asset pricing using autoencoders based on autoencoder asset pricing models https www aqr com insights research working paper autoencoder asset pricing models by shihao gu bryan t kelly and dacheng xiu 2019 and chapter 21 21 gans for synthetic time series shows how to create synthetic training data using generative adversarial networks based on time series generative adversarial networks https papers nips cc paper 8789 time series generative adversarial networks by jinsung yoon daniel jarrett and mihaela van der schaar 2019 all applications now use the latest available at the time of writing software versions such as pandas 1 0 and tensorflow 2 2 there is also a customized version of zipline that makes it easy to include machine learning model predictions when designing a trading strategy installation and data sources for instructions on using a docker image or setting up various conda environments to install the packages used in the notebooks see here installation readme md to download and preprocess many of the data sources used in this book see create datasets data create datasets ipynb chapter summary the book https www amazon com machine learning algorithmic trading alternative dp 1839217715 pf rd r gzh2xz35gb3bet09pcca pf rd p c5b6893a 24f2 4a59 9d4b aff5065c90ec pd rd r 91a679c7 f069 4a6e bdbb a2b3f548f0c8 pd rd w 2b0q0 pd rd wg gmy5s ref pd gw ci mcx mr hp d has four parts that address different challenges that arise when sourcing and working with market fundamental and alternative data sourcing developing ml solutions to various predictive tasks in the trading context and designing and evaluating a trading strategy that relies on predictive signals generated by an ml model the directory for each chapter contains a readme with additional information on content code examples and additional resources part 1 from data to strategy development part 1 from data to strategy development 01 machine learning for trading from idea to execution 01 machine learning for trading from idea to execution 02 market fundamental data sources and techniques 02 market fundamental data sources and techniques 03 alternative data for finance categories and use cases 03 alternative data for finance categories and use cases 04 financial feature engineering how to research alpha factors 04 financial feature engineering how to research alpha factors 05 portfolio optimization and performance evaluation 05 portfolio optimization and performance evaluation part 2 machine learning for trading fundamentals part 2 machine learning for trading fundamentals 06 the machine learning process 06 the machine learning process 07 linear models from risk factors to return forecasts 07 linear models from risk factors to return forecasts 08 the ml4t workflow from model to strategy backtesting 08 the ml4t workflow from model to strategy backtesting 09 time series models for volatility forecasts and statistical arbitrage 09 time series models for volatility forecasts and statistical arbitrage 10 bayesian ml dynamic sharpe ratios and pairs trading 10 bayesian ml dynamic sharpe ratios and pairs trading 11 random forests a long short strategy for japanese stocks 11 random forests a long short strategy for japanese stocks 12 boosting your trading strategy 12 boosting your trading strategy 13 data driven risk factors and asset allocation with unsupervised learning 13 data driven risk factors and asset allocation with unsupervised learning part 3 natural language processing for trading part 3 natural language processing for trading 14 text data for trading sentiment analysis 14 text data for trading sentiment analysis 15 topic modeling summarizing financial news 15 topic modeling summarizing financial news 16 word embeddings for earnings calls and sec filings 16 word embeddings for earnings calls and sec filings part 4 deep reinforcement learning part 4 deep reinforcement learning 17 deep learning for trading 17 deep learning for trading 18 cnn for financial time series and satellite images 18 cnn for financial time series and satellite images 19 rnn for multivariate time series and sentiment analysis 19 rnn for multivariate time series and sentiment analysis 20 autoencoders for conditional risk factors and asset pricing 20 autoencoders for conditional risk factors and asset pricing 21 generative adversarial nets for synthetic time series data 21 generative adversarial nets for synthetic time series data 22 deep reinforcement learning building a trading agent 22 deep reinforcement learning building a trading agent 23 conclusions and next steps 23 conclusions and next steps 24 appendix alpha factor library 24 appendix alpha factor library part 1 from data to strategy development the first part provides a framework for developing trading strategies driven by machine learning ml it focuses on the data that power the ml algorithms and strategies discussed in this book outlines how to engineer and evaluates features suitable for ml models and how to manage and measure a portfolio s performance while executing a trading strategy 01 machine learning for trading from idea to execution this chapter 01 machine learning for trading explores industry trends that have led to the emergence of ml as a source of competitive advantage in the investment industry we will also look at where ml fits into the investment process to enable algorithmic trading strategies more specifically it covers the following topics key trends behind the rise of ml in the investment industry the design and execution of a trading strategy that leverages ml popular use cases for ml in trading 02 market fundamental data sources and techniques this chapter 02 market and fundamental data shows how to work with market and fundamental data and describes critical aspects of the environment that they reflect for example familiarity with various order types and the trading infrastructure matter not only for the interpretation of the data but also to correctly design backtest simulations we also illustrate how to use python to access and manipulate trading and financial statement data practical examples demonstrate how to work with trading data from nasdaq tick data and algoseek minute bar data with a rich set of attributes capturing the demand supply dynamic that we will later use for an ml based intraday strategy we also cover various data provider apis and how to source financial statement information from the sec p align center img src https i imgur com enaso0c png title order book width 50 p in particular this chapter covers how market data reflects the structure of the trading environment working with intraday trade and quotes data at minute frequency reconstructing the limit order book from tick data using nasdaq itch summarizing tick data using various types of bars working with extensible business reporting language xbrl encoded electronic filings parsing and combining market and fundamental data to create a p e series how to access various market and fundamental data sources using python 03 alternative data for finance categories and use cases this chapter 03 alternative data outlines categories and use cases of alternative data describes criteria to assess the exploding number of sources and providers and summarizes the current market landscape it also demonstrates how to create alternative data sets by scraping websites such as collecting earnings call transcripts for use with natural language processing nlp and sentiment analysis algorithms in the third part of the book more specifically this chapter covers which new sources of signals have emerged during the alternative data revolution how individuals business and sensors generate a diverse set of alternative data important categories and providers of alternative data evaluating how the burgeoning supply of alternative data can be used for trading working with alternative data in python such as by scraping the internet 04 financial feature engineering how to research alpha factors if you are already familiar with ml you know that feature engineering is a crucial ingredient for successful predictions it matters at least as much in the trading domain where academic and industry researchers have investigated for decades what drives asset markets and prices and which features help to explain or predict price movements p align center img src https i imgur com ucu4huo png width 70 p this chapter 04 alpha factor research outlines the key takeaways of this research as a starting point for your own quest for alpha factors it also presents essential tools to compute and test alpha factors highlighting how the numpy pandas and ta lib libraries facilitate the manipulation of data and present popular smoothing techniques like the wavelets and the kalman filter that help reduce noise in data after reading it you will know about which categories of factors exist why they work and how to measure them creating e alpha factors using numpy pandas and ta lib how to denoise data using wavelets and the kalman filter using e zipline offline and on quantopian to test individual and multiple alpha factors how to use alphalens to evaluate predictive performance using among other metrics the information coefficient 05 portfolio optimization and performance evaluation alpha factors generate signals that an algorithmic strategy translates into trades which in turn produce long and short positions the returns and risk of the resulting portfolio determine whether the strategy meets the investment objectives p align center img src https i imgur com e2h63zb png width 65 p there are several approaches to optimize portfolios these include the application of machine learning ml to learn hierarchical relationships among assets and treat them as complements or substitutes when designing the portfolio s risk profile this chapter 05 strategy evaluation covers how to measure portfolio risk and return managing portfolio weights using mean variance optimization and alternatives using machine learning to optimize asset allocation in a portfolio context simulating trades and create a portfolio based on alpha factors using zipline how to evaluate portfolio performance using pyfolio part 2 machine learning for trading fundamentals the second part covers the fundamental supervised and unsupervised learning algorithms and illustrates their application to trading strategies it also introduces the quantopian platform that allows you to leverage and combine the data and ml techniques developed in this book to implement algorithmic strategies that execute trades in live markets 06 the machine learning process this chapter 06 machine learning process kicks off part 2 that illustrates how you can use a range of supervised and unsupervised ml models for trading we will explain each model s assumptions and use cases before we demonstrate relevant applications using various python libraries there are several aspects that many of these models and their applications have in common this chapter covers these common aspects so that we can focus on model specific usage in the following chapters it sets the stage by outlining how to formulate train tune and evaluate the predictive performance of ml models as a systematic workflow the content includes p align center img src https i imgur com 5qiscle png width 65 p how supervised and unsupervised learning from data works training and evaluating supervised learning models for regression and classification tasks how the bias variance trade off impacts predictive performance how to diagnose and address prediction errors due to overfitting using cross validation to optimize hyperparameters with a focus on time series data why financial data requires additional attention when testing out of sample 07 linear models from risk factors to return forecasts linear models are standard tools for inference and prediction in regression and classification contexts numerous widely used asset pricing models rely on linear regression regularized models like ridge and lasso regression often yield better predictions by limiting the risk of overfitting typical regression applications identify risk factors that drive asset returns to manage risks or predict returns classification problems on the other hand include directional price forecasts p align center img src https i imgur com 3ph6jma png width 65 p chapter 07 07 linear models covers the following topics how linear regression works and which assumptions it makes training and diagnosing linear regression models using linear regression to predict stock returns use regularization to improve the predictive performance how logistic regression works converting a regression into a classification problem 08 the ml4t workflow from model to strategy backtesting this chapter 08 ml4t workflow presents an end to end perspective on designing simulating and evaluating a trading strategy driven by an ml algorithm we will demonstrate in detail how to backtest an ml driven strategy in a historical market context using the python libraries backtrader https www backtrader com and zipline https www zipline io index html the ml4t workflow ultimately aims to gather evidence from historical data that helps decide whether to deploy a candidate strategy in a live market and put financial resources at risk a realistic simulation of your strategy needs to faithfully represent how security markets operate and how trades execute also several methodological aspects require attention to avoid biased results and false discoveries that will lead to poor investment decisions p align center img src https i imgur com r9o0fn3 png width 65 p more specifically after working through this chapter you will be able to plan and implement end to end strategy backtesting understand and avoid critical pitfalls when implementing backtests discuss the advantages and disadvantages of vectorized vs event driven backtesting engines identify and evaluate the key components of an event driven backtester design and execute the ml4t workflow using data sources at minute and daily frequencies with ml models trained separately or as part of the backtest use zipline and backtrader to design and evaluate your own strategies 09 time series models for volatility forecasts and statistical arbitrage this chapter 09 time series models focuses on models that extract signals from a time series history to predict future values for the same time series time series models are in widespread use due to the time dimension inherent to trading it presents tools to diagnose time series characteristics such as stationarity and extract features that capture potentially useful patterns it also introduces univariate and multivariate time series models to forecast macro data and volatility patterns finally it explains how cointegration identifies common trends across time series and shows how to develop a pairs trading strategy based on this crucial concept p align center img src https i imgur com cgllgj0 png width 90 p in particular it covers how to use time series analysis to prepare and inform the modeling process estimating and diagnosing univariate autoregressive and moving average models building autoregressive conditional heteroskedasticity arch models to predict volatility how to build multivariate vector autoregressive models using cointegration to develop a pairs trading strategy 10 bayesian ml dynamic sharpe ratios and pairs trading bayesian statistics allows us to quantify uncertainty about future events and refine estimates in a principled way as new information arrives this dynamic approach adapts well to the evolving nature of financial markets bayesian approaches to ml enable new insights into the uncertainty around statistical metrics parameter estimates and predictions the applications range from more granular risk management to dynamic updates of predictive models that incorporate changes in the market environment p align center img src https i imgur com qoupidv png width 80 p more specifically this chapter 10 bayesian machine learning covers how bayesian statistics applies to machine learning probabilistic programming with pymc3 defining and training machine learning models using pymc3 how to run state of the art sampling methods to conduct approximate inference bayesian ml applications to compute dynamic sharpe ratios dynamic pairs trading hedge ratios and estimate stochastic volatility 11 random forests a long short strategy for japanese stocks this chapter 11 decision trees random forests applies decision trees and random forests to trading decision trees learn rules from data that encode nonlinear input output relationships we show how to train a decision tree to make predictions for regression and classification problems visualize and interpret the rules learned by the model and tune the model s hyperparameters to optimize the bias variance tradeoff and prevent overfitting the second part of the chapter introduces ensemble models that combine multiple decision trees in a randomized fashion to produce a single prediction with a lower error it concludes with a long short strategy for japanese equities based on trading signals generated by a random forest model p align center img src https i imgur com s4s0rou png width 80 p in short this chapter covers use decision trees for regression and classification gain insights from decision trees and visualize the rules learned from the data understand why ensemble models tend to deliver superior results use bootstrap aggregation to address the overfitting challenges of decision trees train tune and interpret random forests employ a random forest to design and evaluate a profitable trading strategy 12 boosting your trading strategy gradient boosting is an alternative tree based ensemble algorithm that often produces better results than random forests the critical difference is that boosting modifies the data used to train each tree based on the cumulative errors made by the model while random forests train many trees independently using random subsets of the data boosting proceeds sequentially and reweights the data this chapter 12 gradient boosting machines shows how state of the art libraries achieve impressive performance and apply boosting to both daily and high frequency data to backtest an intraday trading strategy p align center img src https i imgur com re0ui0h png width 70 p more specifically we will cover the following topics how does boosting differ from bagging and how did gradient boosting evolve from adaptive boosting design and tune adaptive and gradient boosting models with scikit learn build optimize and evaluate gradient boosting models on large datasets with the state of the art implementations xgboost lightgbm and catboost interpreting and gaining insights from gradient boosting models using shap https github com slundberg shap values and using boosting with high frequency data to design an intraday strategy 13 data driven risk factors and asset allocation with unsupervised learning dimensionality reduction and clustering are the main tasks for unsupervised learning dimensionality reduction transforms the existing features into a new smaller set while minimizing the loss of information a broad range of algorithms exists that differ by how they measure the loss of information whether they apply linear or non linear transformations or the constraints they impose on the new feature set clustering algorithms identify and group similar observations or features instead of identifying new features algorithms differ in how they define the similarity of observations and their assumptions about the resulting groups p align center img src https i imgur com rfk7ucm png width 70 p more specifically this chapter 13 unsupervised learning covers how principal and independent component analysis pca and ica perform linear dimensionality reduction identifying data driven risk factors and eigenportfolios from asset returns using pca effectively visualizing nonlinear high dimensional data using manifold learning using t sne and umap to explore high dimensional image data how k means hierarchical and density based clustering algorithms work using agglomerative clustering to build robust portfolios with hierarchical risk parity part 3 natural language processing for trading text data are rich in content yet unstructured in format and hence require more preprocessing so that a machine learning algorithm can extract the potential signal the critical challenge consists of converting text into a numerical format for use by an algorithm while simultaneously expressing the semantics or meaning of the content the next three chapters cover several techniques that capture language nuances readily understandable to humans so that machine learning algorithms can also interpret them 14 text data for trading sentiment analysis text data is very rich in content but highly unstructured so that it requires more preprocessing to enable an ml algorithm to extract relevant information a key challenge consists of converting text into a numerical format without losing its meaning this chapter 14 working with text data shows how to represent documents as vectors of token counts by creating a document term matrix that in turn serves as input for text classification and sentiment analysis it also introduces the naive bayes algorithm and compares its performance to linear and tree based models in particular in this chapter covers what the fundamental nlp workflow looks like how to build a multilingual feature extraction pipeline using spacy and textblob performing nlp tasks like part of speech tagging or named entity recognition converting tokens to numbers using the document term matrix classifying news using the naive bayes model how to perform sentiment analysis using different ml algorithms 15 topic modeling summarizing financial news this chapter 15 topic modeling uses unsupervised learning to model latent topics and extract hidden themes from documents these themes can generate detailed insights into a large corpus of financial reports topic models automate the creation of sophisticated interpretable text features that in turn can help extract trading signals from extensive collections of texts they speed up document review enable the clustering of similar documents and produce annotations useful for predictive modeling applications include identifying critical themes in company disclosures earnings call transcripts or contracts and annotation based on sentiment analysis or using returns of related assets p align center img src https i imgur com vvsntca png width 60 p more specifically it covers how topic modeling has evolved what it achieves and why it matters reducing the dimensionality of the dtm using latent semantic indexing extracting topics with probabilistic latent semantic analysis plsa how latent dirichlet allocation lda improves plsa to become the most popular topic model visualizing and evaluating topic modeling results running lda using scikit learn and gensim how to apply topic modeling to collections of earnings calls and financial news articles 16 word embeddings for earnings calls and sec filings this chapter 16 word embeddings uses neural networks to learn a vector representation of individual semantic units like a word or a paragraph these vectors are dense with a few hundred real valued entries compared to the higher dimensional sparse vectors of the bag of words model as a result these vectors embed or locate each semantic unit in a continuous vector space embeddings result from training a model to relate tokens to their context with the benefit that similar usage implies a similar vector as a result they encode semantic aspects like relationships among words through their relative location they are powerful features that we will use with deep learning models in the following chapters p align center img src https i imgur com v8w9xll png width 80 p more specifically in this chapter we will cover what word embeddings are and how they capture semantic information how to obtain and use pre trained word vectors which network architectures are most effective at training word2vec models how to train a word2vec model using tensorflow and gensim visualizing and evaluating the quality of word vectors how to train a word2vec model on sec filings to predict stock price moves how doc2vec extends word2vec and helps with sentiment analysis why the transformer s attention mechanism had such an impact on nlp how to fine tune pre trained bert models on financial data part 4 deep reinforcement learning part four explains and demonstrates how to leverage deep learning for algorithmic trading the powerful capabilities of deep learning algorithms to identify patterns in unstructured data make it particularly suitable for alternative data like images and text the sample applications show for exapmle how to combine text and price data to predict earnings surprises from sec filings generate synthetic time series to expand the amount of training data and train a trading agent using deep reinforcement learning several of these applications replicate research recently published in top journals 17 deep learning for trading this chapter 17 deep learning presents feedforward neural networks nn and demonstrates how to efficiently train large models using backpropagation while managing the risks of overfitting it also shows how to use tensorflow 2 0 and pytorch and how to optimize a nn architecture to generate trading signals in the following chapters we will build on this foundation to apply various architectures to different investment applications with a focus on alternative data these include recurrent nn tailored to sequential data like time series or natural language and convolutional nn particularly well suited to image data we will also cover deep unsupervised learning such as how to create synthetic data using generative adversarial networks gan moreover we will discuss reinforcement learning to train agents that interactively learn from their environment p align center img src https i imgur com 5cet0fi png width 70 p in particular this chapter will cover how dl solves ai challenges in complex domains key innovations that have propelled dl to its current popularity how feedforward networks learn representations from data designing and training deep neural networks nns in python implementing deep nns using keras tensorflow and pytorch building and tuning a deep nn to predict asset returns designing and backtesting a trading strategy based on deep nn signals 18 cnn for financial time series and satellite images cnn architectures continue to evolve this chapter describes building blocks common to successful applications demonstrates how transfer learning can speed up learning and how to use cnns for object detection cnns can generate trading signals from images or time series data satellite data can anticipate commodity trends via aerial images of agricultural areas mines or transport networks camera footage can help predict consumer activity we show how to build a cnn that classifies economic activity in satellite images cnns can also deliver high quality time series classification results by exploiting their structural similarity with images and we design a strategy based on time series data formatted like images p align center img src https i imgur com pllqv0m png width 60 p more specifically this chapter 18 convolutional neural nets covers how cnns employ several building blocks to efficiently model grid like data training tuning and regularizing cnns for images and time series data using tensorflow using transfer learning to streamline cnns even with fewer data designing a trading strategy using return predictions by a cnn trained on time series data formatted like images how to classify economic activity based on satellite images 19 rnn for multivariate time series and sentiment analysis recurrent neural networks rnns compute each output as a function of the previous output and new data effectively creating a model with memory that shares parameters across a deeper computational graph prominent architectures include long short term memory lstm and gated recurrent units gru that address the challenges of learning long range dependencies rnns are designed to map one or more input sequences to one or more output sequences and are particularly well suited to natural language they can also be applied to univariate and multivariate time series to predict market or fundamental data this chapter covers how rnn can model alternative text data using the word embeddings that we covered in chapter 16 to classify the sentiment expressed in documents p align center img src https i imgur com e9foapg png width 60 p more specifically this chapter addresses how recurrent connections allow rnns to memorize patterns and model a hidden state unrolling and analyzing the computational graph of rnns how gated units learn to regulate rnn memory from data to enable long range dependencies designing and training rnns for univariate and multivariate time series in python how to learn word embeddings or use pretrained word vectors for sentiment analysis with rnns building a bidirectional rnn to predict stock returns using custom word embeddings 20 autoencoders for conditional risk factors and asset pricing this chapter 20 autoencoders for conditional risk factors shows how to leverage unsupervised deep learning for trading we also discuss autoencoders namely a neural network trained to reproduce the input while learning a new representation encoded by the parameters of a hidden layer autoencoders have long been used for nonlinear dimensionality reduction leveraging the nn architectures we covered in the last three chapters we replicate a recent aqr paper that shows how autoencoders can underpin a trading strategy we will use a deep neural network that relies on an autoencoder to extract risk factors and predict equity returns conditioned on a range of equity attributes p align center img src https i imgur com acme0ud png width 60 p more specifically in this chapter you will learn about which types of autoencoders are of practical use and how they work building and training autoencoders using python using autoencoders to extract data driven risk factors that take into account asset characteristics to predict returns 21 generative adversarial nets for synthetic time series data this chapter introduces generative adversarial networks gan gans train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data the goal is to yield a generative model capable of producing synthetic samples representative of this class while most popular with image data gans have also been used to generate synthetic time series data in the medical domain subsequent experiments with financial data explored whether gans can produce alternative price trajectories useful for ml training or strategy backtests we replicate the 2019 neurips time series gan paper to illustrate the approach and demonstrate the results p align center img src https i imgur com w1rp89k png width 60 p more specifically in this chapter you will learn about how gans work why they are useful and how they could be applied to trading designing and training gans using tensorflow 2 generating synthetic financial data to expand the inputs available for training ml models and backtesting 22 deep reinforcement learning building a trading agent reinforcement learning rl models goal directed learning by an agent that interacts with a stochastic environment rl optimizes the agent s decisions concerning a long term objective by learning the value of states and actions from a reward signal the ultimate goal is to derive a policy that encodes behavioral rules and maps states to actions this chapter 22 deep reinforcement learning shows how to formulate and solve an rl problem it covers model based and model free methods introduces the openai gym environment and combines deep learning with rl to train an agent that navigates a complex environment finally we ll show you how to adapt rl to algorithmic trading by modeling an agent that interacts with the financial market while trying to optimize an objective function p align center img src https i imgur com lg0ofbz png width 60 p more specifically this chapter will cover define a markov decision problem mdp use value and policy iteration to solve an mdp apply q learning in an environment with discrete states and actions build and train a deep q learning agent in a continuous environment use the openai gym to design a custom market environment and train an rl agent to trade stocks 23 conclusions and next steps in this concluding chapter we will briefly summarize the essential tools applications and lessons learned throughout the book to avoid losing sight of the big picture after so much detail we will then identify areas that we did not cover but would be worth focusing on as you expand on the many machine learning techniques we introduced and become productive in their daily use in sum in this chapter we will review key takeaways and lessons learned point out the next steps to build on the techniques in this book suggest ways to incorporate ml into your investment process 24 appendix alpha factor library throughout this book we emphasized how the smart design of features including appropriate preprocessing and denoising typically leads to an effective strategy this appendix synthesizes some of the lessons learned on feature engineering and provides additional information on this vital topic to this end we focus on the broad range of indicators implemented by ta lib see chapter 4 04 alpha factor research and worldquant s 101 formulaic alphas https arxiv org pdf 1601 00991 pdf paper kakushadze 2016 which presents real life quantitative trading factors used in production with an average holding period of 0 6 6 4 days this chapter covers how to compute several dozen technical indicators using ta lib and numpy pandas creating the formulaic alphas describe in the above paper and evaluating the predictive quality of the results using various metrics from rank correlation and mutual information to feature importance shap values and alphalens download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781839217715 https packt link free ebook 9781839217715 a p | ai |
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Image-Filtering-Microservice | 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 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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Stanford-CS224n-NLP | stanford cs224n natural language processing with deep learning the course notes about stanford cs224n winter 2019 using pytorch some general notes i ll write in my deep learning practice repository https github com daviddwlee84 deeplearningpractice course related links course main page winter 2019 https web stanford edu class archive cs cs224n cs224n 1194 latest http web stanford edu class cs224n lecture videos https www youtube com playlist list ploromvodv4rohcuxmzknm7j3fvwbby42z stanford online hub cs224n http onlinehub stanford edu cs224 schedule week lectures assignments 2019 7 1 7 7 introduction and word vectors lecture 1 introduction and word vectors word vectors 2 and word senses lecture 2 word vectors 2 and word senses assignment 1 assignment 1 exploring word vectors 2019 7 8 7 14 word window classification neural networks and matrix calculus lecture 3 word window classification neural networks and matrix calculus 2019 7 15 7 21 backpropagation and computation graphs lecture 4 backpropagation and computation graphs assignment 2 assignment 2 word2vec 2019 10 21 10 27 linguistic structure dependency parsing lecture 5 linguistic structure dependency parsing 2019 10 28 11 3 recurrent neural networks and language models lecture 6 the probability of a sentence recurrent neural networks and language models assignment 3 assignment 3 dependency parsing 2019 11 4 11 10 vanishing gradients and fancy rnns lecture 7 vanishing gradients and fancy rnns machine translation seq2seq and attention lecture 8 machine translation seq2seq and attention assignment 4 assignment 4 neural machine translation 2019 11 11 11 17 transformers and self attention for generative models lecture 14 transformers and self attention for generative models modeling contexts of use contextual representations and pretraining lecture 13 modeling contexts of use contextual representations and pretraining 2019 11 18 11 24 practical tips for projects lecture 9 practical tips for final projects question answering lecture 10 question answering and the default final project convnets for nlp lecture 11 convnets for nlp subword models lecture 12 information from parts of words subword models assignment 5 assignment 5 character based neural machine translation 2019 11 25 12 1 project question answering question answering on squad natural language generation lecture 15 natural language generation 2019 12 2 12 8 project question answering question answering on squad 2019 12 9 12 15 reference in language and coreference resolution lecture 16 reference in language and coreference resolution 2020 1 13 1 19 multitask learning a general model for nlp lecture 17 multitask learning a general model for nlp lecture 1 x introduction and word vectors 2 x word vectors 2 and word senses 3 x word window classification neural networks and matrix calculus 4 x backpropagation and computation graphs 5 x linguistic structure dependency parsing 6 x the probability of a sentence recurrent neural networks and language models 7 x vanishing gradients and fancy rnns 8 x machine translation seq2seq and attention 9 x practical tips for final projects default final project 10 x question answering and the default final project default final project 11 x convnets for nlp 12 x information from parts of words subword models assignment 5 13 x modeling contexts of use contextual representations and pretraining elmo bert 14 x transformers and self attention for generative models self attention transformer 15 x natural language generation 16 x reference in language and coreference resolution 17 x multitask learning a general model for nlp 18 constituency parsing and tree recursive neural networks todo 19 safety bias and fairness 20 future of nlp deep learning assignment 1 x exploring word vectors 2 x word2vec 1 x code 2 x written 3 x dependency parsing 1 x code 2 x written 4 x nerual machine translation 1 x code 2 x written 5 character based neural machine translation 1 x code 2 written todo project 1 question answering default 2 summerization paper reading x word2vec negative sampling glove improveing distrubutional similarity embedding evaluation methods x transformer x elmo x bert fasttext derivation backprop lectures lecture 1 introduction and word vectors slides coursematerials slides cs224n 2019 lecture01 wordvecs1 pdf notes coursematerials notes cs224n 2019 notes01 wordvecs1 pdf readings word2vec tutorial the skip gram model http mccormickml com 2016 04 19 word2vec tutorial the skip gram model efficient estimation of word representations in vector space original word2vec paper coursematerials readings word2vec pdf distributed representations of words and phrases and their compositionality negative sampling paper coursematerials readings negativesampling pdf gensim example coursematerials gensimwordvectorvisualization ipynb preparing embedding download this https nlp stanford edu data glove 6b zip zip file and unzip the glove 6b d txt files into embedding glove directory outline introduction to word2vec objective function prediction function how to train it optimization gradient descent chain rule lecture 2 word vectors 2 and word senses slides coursematerials slides cs224n 2019 lecture02 wordvecs2 pdf notes coursematerials notes cs224n 2019 notes02 wordvecs2 pdf readings glove global vectors for word representation original glove paper coursematerials readings glove pdf improving distributional similarity with lessons learned from word embeddings coursematerials readings improvingdistributionalsimilarity pdf evaluation methods for unsupervised word embeddings coursematerials readings embeddingevaluationmethods pdf additional readings a latent variable model approach to pmi based word embeddings coursematerials additional pmi basedwordembeddings pdf linear algebraic structure of word senses with applications to polysemy coursematerials additional linearalgebraicstructureofwordsenses pdf on the dimensionality of word embedding coursematerials additional dimensionalityofwordembedding pdf outline more detail to word2vec skip grams sg continuous bag of words cbow similarity visualization co occurrence matrix svd lsa vs embedding evaluation on word vectors intrinsic extrinsic cs 168 the modern algorithmic toolbox https web stanford edu class cs168 index html for svd lecture 3 word window classification neural networks and matrix calculus slides coursematerials slides cs224n 2019 lecture03 neuralnets pdf convnetjs https cs stanford edu people karpathy convnetjs matrix calculus coursematerials other gradient notes pdf notes coursematerials notes cs224n 2019 notes03 neuralnets pdf readings cs231n notes on backprop https cs231n github io optimization 2 review of differential calculus coursematerials other review differential calculus pdf additional readings natural language processing almost from scratch coursematerials other nlpfromscratch pdf outline some basic idea of nlp tasks matrix calculus jacobian matrix shape convention loss softmax cross entropy lecture 4 backpropagation and computation graphs slides coursematerials slides cs224n 2019 lecture04 backprop pdf notes coursematerials notes cs224n 2019 notes03 neuralnets pdf same as lecture 3 readings cs231n notes on network architectures https cs231n github io neural networks 1 learning representations by backpropagating errors coursematerials other backprop old pdf derivatives backpropagation and vectorization coursematerials other derivatives pdf yes you should understand backprop https medium com karpathy yes you should understand backprop e2f06eab496b outline computational graph backprop forwardprop introducing regularization to prevent overfitting non linearity activation functions practical tips parameter initialization optimizers plain sgd more sophisticated adaptive optimizers learing rates lecture 5 linguistic structure dependency parsing slides coursematerials slides cs224n 2019 lecture05 dep parsing pdf notes coursematerials notes cs224n 2019 notes04 dependencyparsing pdf readings incrementality in deterministic dependency parsing coursematerials readings incrementality in deterministic dependency parsing pdf a fast and accurate dependency parser using neural networks dependency parsing https www morganclaypool com doi abs 10 2200 s00169ed1v01y200901hlt002 globally normalized transition based neural networks coursematerials readings globally normalized transition based neural networks pdf universal stanford dependencies a cross linguistic typology coursematerials readings universal dependencies a cross linguistic typology pdf x universal dependencies website https universaldependencies org outline methods of dependency parsing dynamic programming complexity o n graph algorithm create a minimum spanning tree for a sentence constraint satisfaction edges are eliminated that don t satisfy hard constraints transition based parsing deterministic dependency parsing greedy choice of attachments guided by machine learning classifier complexity o n operations of the shift reduce parser shift left arc right arc attachment errors prepositional phrase attachment errors verb phrase attachment errors modifier attachment errors coordination attachment errors mentioned cs103 cs228 lecture 6 the probability of a sentence recurrent neural networks and language models slides coursematerials slides cs224n 2019 lecture06 rnnlm pdf notes coursematerials notes cs224n 2019 notes05 lm rnn pdf readings n gram language models coursematerials readings ch3 n gram language models pdf textbook chapter the unreasonable effectiveness of recurrent neural networks https karpathy github io 2015 05 21 rnn effectiveness blog post overview sequence modeling recurrent and recursive neural nets http www deeplearningbook org contents rnn html sections 10 1 and 10 2 on chomsky and the two cultures of statistical learning http norvig com chomsky html n gram language model fixed window neural language model vanilla rnn language modeling the task of predicting the next word given the words so far language model a system that produces the probability distribution for the next candidate word conditional language modeling the task of predicting the next word given the words so far and also some other input x machine translation x source sentence y target sentence summarization x input text y summarized text dialogue x dialogue history y next utterance lecture 7 vanishing gradients and fancy rnns slides coursematerials slides cs224n 2019 lecture07 fancy rnn pdf notes coursematerials notes cs224n 2019 notes05 lm rnn pdf same as lecture 6 readings sequence modeling recurrent and recursive neural nets http www deeplearningbook org contents rnn html textbook sections 10 3 10 5 10 7 10 12 learning long term dependencies with gradient descent is difficult coursematerials readings tnn 94 gradient pdf one of the original vanishing gradient papers on the difficulty of training recurrent neural networks coursematerials readings on the difficulty of training recurrent neural networks pdf proof of vanishing gradient problem vanishing gradients jupyter notebook https web stanford edu class archive cs cs224n cs224n 1174 lectures vanishing grad example html demo for feedforward networks x understanding lstm networks https colah github io posts 2015 08 understanding lstms blog post overview vanishing gradient lstm and gru lecture 8 machine translation seq2seq and attention slides coursematerials slides cs224n 2019 lecture08 nmt pdf notes coursematerials notes cs224n 2019 notes06 nmt seq2seq attention pdf readings statistical machine translation slides cs224n 2015 https web stanford edu class archive cs cs224n cs224n 1162 syllabus shtml lectures 2 3 4 statistical machine translation https www cambridge org core books statistical machine translation 94eadf9f680558e13be759997553cde5 book by philipp koehn bleu a method for automatic evaluation of machine translate coursematerials other bleu pdf original paper sequence to sequence learning with neural networks coursematerials readings sequence to sequence learning with neural networks pdf original seq2seq nmt paper sequence transduction with recurrent neural networks coursematerials readings sequence transduction with recurrent neural networks pdf early seq2seq speech recognition paper neural machine translation by jointly learning to align and translate coursematerials readings neural machine translation by jointly learning to align and translate pdf original seq2seq attention paper attention and augmented recurrent neural networks https distill pub 2016 augmented rnns blog post overview massive exploration of neural machine translation architectures coursematerials readings massive exploration of neural machine translation architectures pdf practical advice for hyperparameter choices training method teacher forcing during training we feed the gold aka reference target sentence into the decoder regardless of what the decoder predicts during testing decoding beam search vs greedy decoding decoding algorithm an algorithm you use to generate text from your language model greedy decoding lack of backtracking on each step take the most probable word i e argmax use that as the next word and feed it as input on the next step keep going until you produce end or reach some max length beam search aims to find high probability sequence by tracking multiple possible sequences at once on each step of decoder keep track of the k beam size most probable partial sequences hypotheses after you reach some stopping criterion get n complete hypotheses each stop when reach max depth produce end choose the sequence with the highest probability with score normalization lecture 13 modeling contexts of use contextual representations and pretraining slides coursematerials slides cs224n 2019 lecture13 contextual representations pdf readings the illustrated bert elmo and co how nlp cracked transfer learning https jalammar github io illustrated bert contextual word representations a contextual introduction coursematerials readings contextual word representations a contextual introduction pdf elmo bert lecture 14 transformers and self attention for generative models guest lecture slides coursematerials slides cs224n 2019 lecture14 transformers pdf readings attention is all you need coursematerials readings transformer pdf the annotated transformer http nlp seas harvard edu 2018 04 03 attention html github http github com harvardnlp annotated transformer image transformer coursematerials readings image transformer pdf music transformer generating music with long term structure coursematerials readings music transformer pdf self attention transformer lecture 9 practical tips for final projects slides coursematerials slides cs224n 2019 lecture09 final projects pdf notes projects final project practical tips pdf good notes about finding existing research datasets and tasks readings practical methodology https www deeplearningbook org contents guidelines html deep learning book chapter vanishing gradient lstm gru again lecture 10 question answering and the default final project slides coursematerials slides cs224n 2019 lecture10 qa pdf notes coursematerials notes cs224n 2019 notes07 qa pdf some more attention mentioned cs 276 information retrieval and web search http web stanford edu class cs276 quick notes about qa qa types factoid qa answer is an ner some clear semantic type entity extractive qa answer must be a span a sub sequence of words in the passage e g squad 1 x defect all questions have an answer in the paragraph turned into a kind of a ranking task extractive qa noanswer some question might have no answer in the paragraph e g squad 2 0 limitation only span based answers no yes no counting implicit why open domain qa e g drqa 1704 00051 reading wikipedia to answer open domain questions https arxiv org abs 1704 00051 lecture 11 convnets for nlp slides coursematerials slides cs224n 2019 lecture11 convnets pdf notes coursematerials notes cs224n 2019 notes08 cnn pdf readings convolutional neural networks for sentence classification https arxiv org abs 1408 5882 a convolutional neural network for modelling sentences https arxiv org abs 1404 2188 mentioned cs231n convolutional neural networks for visual recognition http cs231n stanford edu lot of common technique nowadays model comparison bag of vectors take the word vectors and averaging them good baseline better have followed by a few relu window model good for single word classification for problems that don t need wide context e g pos ner cnns good for classification need zero padding for shorter phrases easy to parallelize rnns cognitively plausible reading from left to right not best for classification if just use last state much slower than cnns good for sequence tagging great for language models and can be amazing with attention mechanism dropout for regularization prevent overfitting gives 2 4 accuracy improvement gated units used vertically shortcut connection is needed for very deep networks to work residual block highway block batchnorm z transform zero mean and unit variance lecture 12 information from parts of words subword models slides coursematerials slides cs224n 2019 lecture12 subwords pdf readings achieving open vocabulary neural machine translation with hybrid word character models https arxiv org abs 1604 00788 fasttext lecture 15 natural language generation slides coursematerials slides cs224n 2019 lecture15 nlg pdf outline decoding mehtods greedy decoding beam search sampling based decoding good for open ended creative generation poetry stories pure sampling like greedy decoding but sample instead of argmax top n sampling like pure sampling but truncate the probability distribution softmax temperature another way to control diversity nlg tasks machine translation abstractive summarization evaluation rouge dialogue chit chat task based creative writing storytelling poetry generation freefrom question answering image captioning nlg evaluation metrics word overlap based metrics bleu rouge meteor f1 perplexity doesn t tell you anything about generation word embedding based metrics human evaluation lecture 16 reference in language and coreference resolution slides coursematerials slides cs224n 2019 lecture16 coref pdf outline coreference resolution identify all mentions that refer to the same real world entity application full text understanding machine translation dialogue systems step pipelined system 1 detect the mentions using other nlp system 2 cluster the mentions end to end system model rule based pronomial anaphora resolution can t solve sentences which have identical syntactic structure mention pair binary classifier coreferent or not for every pair of mentions custering 1 pick a threshold and add coreference links when above 2 take the transitive closure to get the clustering mention ranking 1 assign each mention its highest scoring candidate antecedent 2 add dummy na mention at the front for decline linking clustering agglomerative clustering 1 start with each mention in its own singleton cluster 2 merge a pair of clusters at each step mention span of text referring to some entity 1 pronouns capture use a part of speech tagger 2 named entities capture use a ner system 3 noun phrases capture use a parser especially a constituency parser linguistics stuff coreference two mentions refer to the same entity in the world anaphora when a term anaphor refers to another term antecedent pronominal anaphora coreferential one bridging anaphora not coreferential cataphora when antecedent comes after usually before the anaphor lecture 17 multitask learning a general model for nlp slides coursematerials slides cs224n 2019 lecture17 multitask pdf outline natural language decathlon decanlp https decanlp com reduce subtask to more general task transfer knowledge from the other task maybe then we can do zero shot learning transfer learning salesforce decanlp the natural language decathlon a multitask challenge for nlp https github com salesforce decanlp 3 equivalent supertasks of nlp language modeling predict next word embedding question answering formalism multitask learning as qa training single question answering model for multiple nlp tasks aka questions question answering machine translation summarization natural language inference sentiment classification semantic role labeling relation extraction dialogue semantic parsing commonsense reasoning dialogue framework for tackling more general language understanding multitask learning domain adaptation transfer learning weight sharing pre training fine tuning towards imagenet cnn of nlp zero shot learning assignments assignment 1 exploring word vectors code assignments a1 exploring word vectors ipynb directory assignments a1 outline co occurrance matrix truncated svd pre trained word2vec assignment 2 word2vec handout assignments a2 a2 pdf directory assignments a2 written assignments a2 written assignment2 pdf code assignments a2 code python3 word2vec py check the correctness of word2vec python3 sgd py check the correctness of sgd get datasets sh python3 run py training took 9480 seconds outline train word2vec with skip gram model and negative sampling using stochastic gradient descent related data processing in cs224n assignment 2 word2vec 2019 https medium com ilyarudyak data processing in cs224n assignment 2 word2vec 2019 288bdc8d4cb6 others answer zacbi cs224n 2019 solutions word2vec py https github com zacbi cs224n 2019 solutions blob master assignments a2 word2vec py zeyadzanaty cs224n assignments word2vec py https github com zeyadzanaty cs224n assignments blob master assignment 2 word2vec py assignment 3 dependency parsing a fast and accurate dependency parser using neural networks https nlp stanford edu pubs emnlp2014 depparser pdf handout assignments a3 a3 pdf directory assignments a3 written assignments a3 written assignment3 pdf code assignments a3 code python3 parser transitions py part c check the corretness of transition mechanics python3 parser transitions py part d check the correctness of minibatch parse python3 run py set debug true to test the process debug out log assignments a3 debug out log set debug false to train on the entire dataset train out log assignments a3 train out log best uas on the dev set 88 79 epoch 9 10 best uas on the test set 89 27 outline adam optimizer dropout neural transition based dependency parser a shift reduce parser others answer cs224n 2019 assignment a3 at master luvata cs224n 2019 https github com luvata cs224n 2019 tree master assignment a3 assignment 4 neural machine translation handout assignments a4 a4 pdf asure guide assignments azureguide pdf google drive https docs google com document d 1mhaqvbtpkfegc93hxzpvhkkum1j f1qsyj4x0vktudi edit practical guide to vms assignments practicalvmtips pdf google drive https docs google com document d 1z9st0ivxhq3hxsaompcvbfu5zesmettac9km6lapjxk edit directory assignments a4 written assignments a4 written assignment4 pdf bleu verify assignments a4 written bleu verify ipynb a gentle introduction to calculating the bleu score for text in python https machinelearningmastery com calculate bleu score for text python nltk translate bleu score tilde interactive bleu score evaluator https www letsmt eu bleu aspx input txt code assignments a4 code python3 sanity check py 1d check the correctness of encode procedure including utils pad sents python3 sanity check py 1e check the correctness of decode procedure including step function preprocess the training data by sh run sh vocab to get the necessary vocabulary test the functionality on cpu train sh run sh train local test sh run sh test local speed about 100 words sec on macbook air 1 8ghz i5 cpu train and test with gpu train sh run sh train test sh run sh test speed about 5000 words sec on nvidia geforce gtx 1080 gpu this will generate model image model bin and optimizers state model bin optim early stop on epoch 13 iter 86000 cum loss 28 94 cum ppl 5 13 cum examples 64000 corpus bleu 22 36579929869114 compare output with references vim do outputs test outputs txt en es data test en open three of them at the same time vim o outputs test outputs txt en es data test en en es data test es other s answer pcyin pytorch nmt a neural machine translation model in pytorch https github com pcyin pytorch nmt assignment 5 character based neural machine translation build a character level convnet handout assignments a5 public a5 pdf directory assignments a5 public written assignments a5 public written assignment5 pdf code assignments a5 public code create the correct vocab files sh run sh vocab vocab tiny q1 json generated vocabulary source 132 words target 132 words source number of word types 128 number of word types w frequency 1 128 target number of word types 130 number of word types w frequency 1 130 vocab tiny q2 json generated vocabulary source 26 words target 32 words source number of word types 128 number of word types w frequency 2 22 target number of word types 130 number of word types w frequency 2 30 vocab json generated vocabulary source 50004 words target 50002 words source number of word types 172418 number of word types w frequency 2 80623 target number of word types 128873 number of word types w frequency 2 64215 sanity checks python3 sanity check py part pre defined 1e 1f 1j 2a 2b 2c 2d customized 1g 1h 1i 1j test the first part code at local sh run sh train local q1 this will run 100 epoches epoch 100 iter 500 cum loss 0 31 cum ppl 1 02 cum examples 200 validation iter 500 dev ppl 1 003381 sh run sh test local q1 the model should overfit corpus bleu 99 29792465574434 99 this will generate outputs test outputs local q1 txt test the second part code at local sh run sh train local q2 epoch 200 iter 1000 cum loss 0 26 cum ppl 1 01 cum examples 200 validation iter 1000 dev ppl 1 003469 sh run sh test local q2 the model should overfit corpus bleu 99 29792465574434 this will generate outputs test outputs local q2 txt train the model with sh run sh train and test the performance with sh run sh test epoch 29 iter 196330 avg loss 90 37 avg ppl 147 15 cum examples 10537 speed 3512 25 words sec time elapsed 29845 45 sec reached maximum number of epochs corpus bleu 24 20035238301319 todo enrich the sanity check of the highway enrich the sanity check of the cnn compare the output with assignment 4 especially the unk words written part projects project proposal projects project proposal instructions pdf milestone instruction projects project milestone instructions pdf project report projects project report instructions pdf project poster video projects project postervideo instructions pdf question answering on squad squad is not an natural language generation task since the answer is extracted from text default final project handout projects questionanswering default final project handout pdf starter code https github com chrischute squad directory projects questionanswering summerization dataset cornell newsroom summarization dataset https summari es metrics rouge recall oriented understudy for gisting evaluation with small scale human eval baseline simplest model logistic regression on unigrams and bigrams averaging word vectors lede 3 baseline book o reilly natural language processing with pytorch recommend in lecture 11 joosthub pytorchnlpbook code and data accompanying natural language processing with pytorch published by o reilly media https github com joosthub pytorchnlpbook nlproc natural language processing https nlproc info course contents backup https github com zhanlaoban cs224n stanford winter 2019 software the stanford natural language processing group https nlp stanford edu software stanford nlp chinese usage https github com liu nlper stanford nlp usage others answer luvata cs224n 2019 https github com luvata cs224n 2019 almost finish all the written part as well zacbi cs224n 2019 solutions https github com zacbi cs224n 2019 solutions didn t finish the written part youngmihuang cs224n exercise https github com youngmihuang cs224n exercise only 2019 a1 a4 coding part observerspy cs224n https github com caijie12138 cs224n 2019 not fully 2019 caijie12138 cs224n 2019 https github com caijie12138 cs224n 2019 not quite the assignment zeyadzanaty cs224n assignments https github com zeyadzanaty cs224n assignments just coding part assignment 2 3 pytorch notes element wise product a b torch mul a b a mul b matrix multiplication a b torch matmul a b torch mm torch bmm runtimeerror view size is not compatible with input tensor s size and stride at least one dimension spans across two contiguous subspaces use reshape instead https github com agrimgupta92 sgan issues 22 issuecomment 452980941 view error only on cpu because tensor cuda automatically makes the tensor contiguous contiguous view okay reshape okay | cs224n cs224n-assignment-solutions cs224nwinter2019 language-models dependency-parsing machine-translation question-answering | ai |
tencent-cloud-iotsdk-package-for-rtt | tencent iot kit for rtthread package 1 tencent iot kit for rtthread package c sdk https github com tencentyun tencent cloud iotsuite embedded c git rthread iot 1 1 sdk sdk architecture https user images githubusercontent com 990858 46805530 dc9f8e00 cd97 11e8 888b 1dd1171bfc1a png 1 2 docs tencent cloud iotsuite embedded c iotsuit ports samples license readme md sconscript rt thread 1 3 sdk c sdk api api include tc iot export h include tc iot export h 1 4 mit license 2 2 1 hal sdk hal hal sdk hal tencent cloud iotsuite embedded c src platform rtthread rtthread package package ports sample tencent cloud iotsuite embedded c examples rtthread tencent iot kit for rtthread package rtthread rtthread sal bsd shell src platform rtthread rtthread rtthread port tc iot hal net c tcp tc iot hal os c tc iot hal timer c tc iot hal tls c tls tc iot hal udp c udp tc iot hal dtls c dtls tc iot get time c ntp utc c sdk hal linux posix posix hal include platform tc iot hal h tc iot hal 3 3 1 rtthread rt thread env menuconfig tencent iotkit shell rt thread online packages iot internet of things iot cloud tencent iotkit tencent cloud sdk for iot platform select auth mode auth by token mqtt 1egtdjqza config product key rt thread dev1 config device name 92b0e676cdd608c4fb56386967613764 config device secret enable mqtt select mqtt data type enable mqtt advanced enable mqtt ssl enable coap enable scenarios enable http version latest tls mbedtls shell rt thread online packages security packages mbedtls an open source portable easy to use readable and flexible ssl library store the aes tables in rom new 2 maximum window size used new 3584 maxium fragment length in bytes new enable a mbedtls client example new enable debug log output new version latest pkgs update 3 2 1 scons target xxx 2 3 3 demo msh mqtt msh shell msh tc mqtt shadow example tc mqtt shadow exmaple tc iot sdk ver 2 6 trace tc iot server init 375 c s shadow get method get passthrough sid 89390000 metadata false trace tc iot mqtt refresh dynamic sign 1186 usename rt thread dev1 password productid iot 1l60dtl0 nonce 1350022348 timestamp 1539578219 signature d4bmuke1nvve1hjapixnxr request username and password for mqtt success trace tc iot shadow check and report 1035 report first time param bool false trace tc iot shadow check and report 1035 report first time param enum 0 trace tc iot shadow check and report 1035 report first time param number 0 000000 trace tc iot shadow check and report 1035 report first time param string trace tc iot shadow check and report 1114 requesting with method update passthrough sid 196c0001 state reported param bool false param enum 0 param number 0 000000 param string trace tc iot report message ack callback 47 s c method reply timestamp 1539578239 payload code 0 status ok passthrough sid 196c0001 coap shell msh tc coap advance example tc coap advance example tc iot sdk version 2 9msh coap server gz coap tencentcloudapi com 5683 trace tc iot coap construct 482 mqtt client buf size 1152 readbuf size 1152 trace tc iot coap rpc 875 send message success sent size 226 trace tc iot coap deserialize 299 received ver 1 type ack tkl 4 code 2 01 created message id 2 trace tc iot coap deserialize 336 option 12 content format trace tc iot coap deserialize 336 option 14 max age trace tc iot coap yield 683 response for request mid 2 total timer left 3730ms s c method reply payload code 0 status ok state desired timestamp 1540456355 sequence 2063 trace coap check and process desired 308 mem json token tc iot max json token count total 2400 used 340 left 2060 trace coap check and process desired 317 payload state desired found trace coap con get rpc handler 408 no desired data since last check http rpc shell msh tc http rpc example tc http rpc example tc iot sdk version 2 9msh trace get timestamp from ntp 220 second 1540455961 client server method get metadata false reported false 3903d58467e1397c3068012a0cdff4b9 devicename collector 1 1 message method get metadata false reported false nonce 509772187 productid iot eib00wp2 timestamp 1540455961 trace tc iot create mqapi rpc json 34 mem b64 buf total 46 used 44 left 2 trace tc iot http mqapi rpc 88 mem sign out tc iot http token request form len total 1024 used 1 left 1023 trace tc iot http mqapi rpc 90 signed request form productid iot eib00wp2 devicename collector 1 1 nonce 509772187 timestamp 1540455961 messag trace tc iot http mqapi rpc 103 http buffer post rpc http 1 1 user agent tciotclient 1 0 host gz auth device iot tencentcloudapi com accept conte trace tc iot network prepare 64 dirtect tcp network intialized trace tc iot http is complete chunk 64 chunk size 194 trace tc iot http is complete chunk 60 last chunk found trace tc iot http mqapi rpc 106 mem http buffer tc iot http token response len total 1024 used 194 left 830 server client method reply payload code 0 status ok state desired timestamp 1540455961 sequence end rpc get server desired data trace check and process desired 141 mem json token tc iot max json token count total 2400 used 340 left 2060 trace check and process desired 150 payload state desired found | os |
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stanford-cs224n | cs224n stanford cs224n natural language processing with deep learning | deep-learning neural-networks natural-language-processing stanford-nlp cs224n | ai |
deeplite-torch-zoo | div align center logo https docs deeplite ai neutrino static content deeplite logo color png deeplite torch zoo is a collection of state of the art efficient computer vision models for embedded applications in pytorch https pytorch org build status https travis ci com deeplite deeplite torch zoo svg token kodd5rkmpjxqdqrcxwiv branch master https travis ci com deeplite deeplite torch zoo codecov https codecov io gh deeplite deeplite torch zoo branch master graph badge svg token avtp3pw5up https codecov io gh deeplite deeplite torch zoo div for information on yolobench click here results yolobench the main features of this library are high level api to create models dataloaders and evaluation functions single interface for sota classification models timm models https github com huggingface pytorch image models pytorchcv models https github com osmr imgclsmob tree master pytorch other sota efficient models edgevit fasternet ghostnetv2 mobileone single interface for sota yolo detectors compatible with ultralytics training https github com ultralytics ultralytics yolov3 v4 v5 v6 3 0 v7 v8 yolo with timm backbones other experimental configs https github com deeplite deeplite torch zoo tree develop deeplite torch zoo src object detection yolov5 configs table of content 1 quick start start 2 installation installation 3 training scripts training scripts 7 contributing contributing 9 credit credit quick start a name start a create a classification model python from deeplite torch zoo import get model list models model get model model name edgevit xs model names for imagenet available via list models imagenet dataset name imagenet dataset name since resnet18 is different for e g imagenet and cifar100 pretrained false if true will try to load a pre trained checkpoint creating a model with 42 classes for transfer learning model get model model name fasternet t0 model names for imagenet available via list models imagenet num classes 42 number of classes for transfer learning dataset name imagenet take weights from checkpoint pre trained on this dataset pretrained false if true will try to load all weights with matching tensor shapes create an object detection model python from deeplite torch zoo import get model model get model model name yolo4n creates a yolov4n model on coco dataset name coco n corresponds to width factor 0 25 depth factor 0 33 pretrained false if true will try to load a pre trained checkpoint one could create a yolo model with timm backbone pan neck and yolov8 decoupled anchor free head like this model get model model name yolo timm fbnetv3 d creates a yolo with fbnetv3 d backbone from timm dataset name coco pretrained false if true will try to load a pre trained checkpoint custom head yolo8 will replace default detection head with yolov8 detection head create pytorch dataloaders python from deeplite torch zoo import get dataloaders dataloaders get dataloaders data root folder with data will be used for download dataset name imagewoof datasets to if applicable num workers 8 number of dataloader workers batch size 64 dataloader batch size train and test dataloaders train train dataloader dataloaders test test dataloader see below for the list of supported datasets the list of supported datasets is available for classification https github com deeplite deeplite torch zoo blob develop docs classification md and object detection https github com deeplite deeplite torch zoo blob develop docs object detection md creating an evaluation function python from deeplite torch zoo import get eval function eval function get eval function model name yolo8s dataset name voc required arg signature is fixed for all eval functions metrics eval function model test dataloader experimental training with patched ultralytics trainer python from deeplite torch zoo trainer import detector model detector model name yolo7n will create a wrapper around yolov7n model yolov7n model with yolov8 detection head model train data voc yaml epochs 100 same arguments as ultralytics trainer alternatively torch model get model model name yolo7n dataset name coco pretrained false custom head yolo8 model detector torch model torch model either model name or torch model model train data voc yaml epochs 100 should be provided installation a name installation a pypi version bash pip install deeplite torch zoo latest version from source bash pip install git https github com deeplite deeplite torch zoo git training scripts a name training scripts a we provide several training scripts as an example of how deeplite torch zoo can be integrated into existing training pipelines modified timm imagenet script https github com deeplite deeplite torch zoo tree develop training scripts classification imagenet support for knowledge distillation training recipes provides a1 a2 a3 usi https github com alibaba miil solving imagenet etc modfied ultralytics classification fine tuning script https github com deeplite deeplite torch zoo tree develop training scripts classification ultralytics modfied ultralytics yolov5 object detector training script https github com deeplite deeplite torch zoo tree develop training scripts object detection contributing a name contributing a we always welcome community contributions to expand the scope of deeplite torch zoo and also to have additional new models and datasets please refer to the documentation https docs deeplite ai neutrino zoo html contribute a model dataset to the zoo for the detailed steps on how to add a model and dataset in general we follow the fork and pull git workflow 1 fork the repo on github 2 clone the project to your own machine 3 commit changes to your own branch 4 push your work back up to your fork 5 submit a pull request so that we can review your changes note be sure to merge the latest from upstream before making a pull request credit a name credit a details summary repositories used to build deeplite torch zoo summary object detection yolov3 implementation ultralytics yolov3 https github com ultralytics yolov3 yolov5 implementation ultralytics yolov5 https github com ultralytics yolov5 flexible yolov5 implementation bobo y flexible yolov5 https github com bobo y flexible yolov5 yolov8 implementation ultralytics ultralytics https github com ultralytics ultralytics yolov7 implementation wongkinyiu yolov7 https github com wongkinyiu yolov7 yolox implementation iscyy yoloair https github com iscyy yoloair westerndigitalcorporation yolov3 in pytorch https github com westerndigitalcorporation yolov3 in pytorch segmentation the implementation of deeplab pytorch deeplab xception https github com jfzhang95 pytorch deeplab xception the implementation of unet scse nyoki mtl pytorch segmentation https github com nyoki mtl pytorch segmentation the implementation of fcn wkentaro pytorch fcn https github com wkentaro pytorch fcn the implementation of unet milesial pytorch unet https github com milesial pytorch unet classification the implementation of models on cifar100 dataset kuangliu pytorch cifar https github com kuangliu pytorch cifar the implementation of mobilenetv1 model on vww dataset qfgaohao pytorch ssd https github com qfgaohao pytorch ssd the implementation of mobilenetv3 model on vww dataset d li14 mobilenetv3 pytorch https github com d li14 mobilenetv3 pytorch dnn building block implementations d li14 mobilenetv2 pytorch https github com d li14 mobilenetv2 pytorch d li14 efficientnetv2 pytorch https github com d li14 efficientnetv2 pytorch apple ml mobileone https github com apple ml mobileone osmr imgclsmob https github com osmr imgclsmob huggingface pytorch image models https github1s com huggingface pytorch image models moskomule senet pytorch https github com moskomule senet pytorch dingxiaoh replknet pytorch https github com dingxiaoh replknet pytorch huawei noah efficient ai backbones https github com huawei noah efficient ai backbones misc torchvision dataset implementations pytorch vision https github com pytorch vision mlp implementation aaron xichen pytorch playground https github com aaron xichen pytorch playground autoaugment implementation deepvoltaire autoaugment https github com deepvoltaire autoaugment cutout implementation uoguelph mlrg cutout https github com uoguelph mlrg cutout robustness measurement image distortions hendrycks robustness https github com hendrycks robustness registry implementation openvinotoolkit openvino tools pot https github com openvinotoolkit openvino blob master tools pot details | ai |
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UMLStateMachines | umlstatemachines repository for the udemy course embedded systems design using uml state machines | os |
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ev3rt-hrp2 | usage please check our website http ev3rt git github io for usage build status status platform build status https travis ci org ev3rt git ev3rt hrp2 svg branch master https travis ci org ev3rt git ev3rt hrp2 ubuntu 14 04 | os |
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CADOMonitoringWebsite-AdminFrontend | frontend this is the frontend developed for the admins of the website where they can see and edit data as needed development server run ng serve for a dev server navigate to http localhost 4200 the app will automatically reload if you change any of the source files code scaffolding run ng generate component component name to generate a new component you can also use ng generate directive pipe service class guard interface enum module build run ng build to build the project the build artifacts will be stored in the dist directory use the prod flag for a production build running unit tests run ng test to execute the unit tests via karma https karma runner github io running end to end tests run ng e2e to execute the end to end tests via protractor http www protractortest org further help to get more help on the angular cli use ng help or go check out the angular cli readme https github com angular angular cli blob master readme md | angular webapp azure frontend | server |
cs590web-fall2023 | our course project is about college events with timely updates page ideas index home events summarize events academic sports fun profile sign up | front_end |
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lab-support-data | ttk4155 lab support data lab support data for ttk4155 embedded and industrial computer systems design project for use in ttk4155 embedded and industrial computer systems design ntnu norwegian university of science and technology | os |
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aemmobile | build status https ci appveyor com api projects status mc3stvfqt98mmlnk branch master svg true https ci appveyor com project adzellman aemmobile ju6lf build status https travis ci org adobe marketing cloud mobile aemmobile svg branch master https travis ci org adobe marketing cloud mobile aemmobile codecov io https codecov io github adobe marketing cloud mobile aemmobile coverage svg branch master https codecov io github adobe marketing cloud mobile aemmobile branch master bithound overall score https www bithound io github adobe marketing cloud mobile aemmobile badges score svg https www bithound io github adobe marketing cloud mobile aemmobile npm https nodei co npm aemm png https nodei co npm aemm aem mobile command line tool for building aem mobile apps with cordova content operating system mac os x ios and android windows android only prerequisites mac os x node v6 2 2 or greater must be installed for ios xcode v7 0 or greater must be installed for android java must be installed https support apple com downloads java for android chrome needs to be installed for debugging html content via chrome inspect windows java sdk http www oracle com technetwork java javase downloads index html visualstudio community 2015 https www visualstudio com en us products visual studio community vs aspx node v6 2 2 or greater https nodejs org en python 2 7 https www python org download releases 2 7 chrome needs to be installed for debugging html content via chrome inspect installation you need npm installed to run the command line tool on windows run commands in powershell instead of command prompt 1 install npm https nodejs org en 2 install aemm sudo npm install g aemm if that doesn t work do the following 1 install npm https nodejs org en 2 get the app from the repo and then use npm install and link git clone https github com adobe marketing cloud mobile aemmobile git cd aemmobile sudo npm g install sudo npm link for android you may see a couple compilation errors related to node gyp when running npm install on mac os x and windows it s safe to ignore them they don t affect the functionalities of this tool if the installation fails due to errors with a specific module troubleshoot by clearing your npm cache and reinstalling sudo npm remove g aemm sudo npm cache clean sudo npm install g aemm this will resolve current issues with the npm async module usage there are 2 types of workflow this tool is designed for developing custom html content developing custom application with custom plugins sudo note command samples below are not always proceeded with sudo some commands on mac will fail without it for instance permissions may not be available to set temporary and necessary environment variables if you encounter errors try using sudo before the command commands for both workflows aemm platform install platform sudo aemm platform install android accept several android sdk license agreements this installs and updates various android sdks build tools and setup system environment for developing android application you may need to open a new terminal to have the new system environment settings take effect sudo aemm platform install ios aemm project create project name or path aemm project create testproject aemm project create path to testproject you must run the following commands inside the directory created with aemm project create project name or path aemm article create articlename aemm article create article1 aemm article create article1 article2 article3 1 developing custom html content aemm app install platform aemm app install ios aemm app install android aemm app install list aemm app install ios 2016 5 you must run the following commands inside the directory created with aemm project create project name or path aemm run platform aemm run ios aemm run android aemm run ios list aemm run ios target iphone 6s 9 2 note the run command without the device parameter will run the application in the emulator simulator 2 developing custom application with custom plugins you must run the following commands inside the directory created with aemm project create project name or path add the platforms you want to target your application for aemm platform add platform aemm platform add android aemm platform add ios add the plugins you want to be included in your application aemm plugin add plugin 0 plugin 1 aemm plugin add cordova plugin device cordova plugin contacts aemm build platform aemm build android aemm build ios device note the device parameter for the build command is for ios only aemm run platform aemm run ios aemm run android aemm run android device aemm run ios list aemm run ios target iphone 6s 9 2 note the run command without the device parameter will run the application in the emulator simulator note aemm was built on cordova and delegates many commands to cordova lib you may experience errors that recommend that you try to run a cordova command in most of such cases please first try to replace cordova with aemm then execute the recommended action | front_end |
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image-to-text | image to text https github com kainstar image to text https blog kainstar moe image to text jpg png gif canvas gif js gif webpack react gif js gif src component style tools app jsx index js index html static js gif worker js gif js webpack webpack basic pic basic gif size pic size gif size pic color gif size pic custom chars gif bash npm i npm run dev bash npm run build github pages bash npm run deploy | front_end |
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Ionic2Do | ionic2do this is a starter template for the ionic2do app from mobile app development with ionic 2 http www ionic2book com published by o reilly press how to use this template this template does not work on its own bash sudo npm install g ionic cordova ionic start ionic2do blank cd ionic2do once you have created the starter ionic application using the steps above download this repo and copy over the src and www directories into the newly created folder replacing the initial src and www directory you will still need to follow any additional steps in the book | front_end |
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wax-blockchain-legacy | welcome to the wax source code repository this software enables businesses to rapidly build and deploy high performance and high security blockchain based applications for developer tips and tutorials refer to our wax blockchain developer hive https developer wax io the worldwide asset exchange wax is a purpose built blockchain and protocol token designed to make e commerce transactions faster easier and safer for all participants the wax blockchain mainnet uses delegated proof of stake dpos as its consensus mechanism and is fully backward compatible with eos the custom features and incentive mechanisms developed by wax are designed to optimize the blockchain s usability in e commerce and encourage voting on guilds and proposals wax is creating a suite of blockchain based tools upon which dapps marketplaces and native non fungible tokens nfts are built these tools include services to support e commerce operations such as an interactive block explorer wallet sso and oauth item creation an rng service interactive item viewers marketplace creation and more the resulting technology is a blockchain architecture that is extremely fast 500 millisecond block times fee less for customers and less expensive for developers and leverages voting rewards to incentivize participation in the selection of block producers and proposals some of the features of wax blockchain include 1 token swap in conjunction with genesis block member rewards https wax io blog introducing the genesis block member program join and receive daily token rewards for 3 years 2 earning staking rewards https wax io blog earn more wax introducing wax block rewards staking and voting guilds and more 3 incentives and mechanics to address voter apathy https wax io blog staking and voting on wax a technical deep dive 4 random number generator native blockchain service https wax io blog how the wax rng smart contract solves common problems for dapp developers 5 wax account account creation voting token swap and balances https wax io blog a sneak peek of wax account features 6 integrated with the opskins customer base and other reasons to run dapps on wax https wax io blog the top 10 reasons to run dapps on wax these features are in addition to the benefits you will find on eosio https github com eosio the wax blockchain is a variant of eosio see wax release notes md wax release notes md for details wax is released under the open source mit license and is offered as is without warranty of any kind express or implied any security provided by the wax software depends in part on how it is used configured and deployed wax is built upon many third party libraries such as wabt apache license and wavm bsd 3 clause which are also provided as is without warranty of any kind without limiting the generality of the foregoing wax makes no representation or guarantee and disclaims all implied warranties and guarantees that wax or any third party libraries will perform as intended or will be free of errors bugs or faulty code both may fail in large or small ways that could completely or partially limit functionality or compromise computer systems if you use or implement wax you do so at your own risk in no event will wax be liable to any party for any damages whatsoever even if it had been advised of the possibility of damage there is no public testnet running currently installation instructions you have several ways to run a node and develop smart contracts on the wax blockchain 1 use our production docker images https cloud docker com u waxteam repository docker waxteam production this is the official and recommended way also the faster one you can run a node in seconds see the mainnet sample https github com worldwide asset exchange wax blockchain tree develop samples mainnet for more information 2 complete our docker quickstart https developer wax io dapps docker quickstart you can also find wax tutorials in the dapp development https developer wax io dapps section of the wax blockchain developer hive https developer wax io 3 currently we are not providing pre compiled packages therefore if you can t use our docker images you will have to compile the source code and install it with the following instructions note building from source is not supported we recommend using our docker images https hub docker com u waxteam instead console git clone https github com worldwide asset exchange wax blockchain git cd wax blockchain git submodule update init recursive build and set the installation directory wax build sh i wax blockchain this will install the blockchain in the previously set installation directory wax install sh optional add the blockchain to your path it requires a console relogin echo export path wax blockchain bin path bashrc if you have previously installed wax blockchain please run the wax uninstall script it is in the directory where you cloned wax blockchain troubleshooting if you have trouble when building the blockchain may be this can help you fc linking problem patches fc readme md running a full node the mainnet https github com worldwide asset exchange wax blockchain tree develop samples mainnet directory contains configuration files and instructions to launch a full node supported operating systems wax currently supports the following operating systems amazon linux 2 centos 7 ubuntu 16 04 ubuntu 18 04 macos 10 14 mojave resources website https wax io blog https wax io blog wax blockchain developer hive https developer wax io community telegram group https t me wax io white paper https wax io uploads wax white paper pdf license mit https github com worldwide asset exchange wax blockchain blob master license | blockchain |
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PyTorchNLPBook | natural language processing with pytorch build intelligent language applications using deep learning br by delip rao and brian mcmahan welcome this is a companion repository for the book natural language processing with pytorch build intelligent language applications using deep learning https www amazon com natural language processing pytorch applications dp 1491978236 table of contents ts get started chapter 1 introduction https github com joosthub pytorchnlpbook tree master chapters chapter 1 pytorch basics chapter 2 a quick tour of nlp chapter 3 foundational components of neural networks https github com joosthub pytorchnlpbook tree master chapters chapter 3 in text examples diving deep into supervised training classifying sentiment of restaurant reviews using a perceptron chapter 4 feed forward networks for nlp https github com joosthub pytorchnlpbook tree master chapters chapter 4 limitations of the perceptron introducing multi layer perceptrons mlps introducing convolutional neural networks cnns surname classification with an mlp surname classification with a cnn chapter 5 embedding words and types https github com joosthub pytorchnlpbook tree master chapters chapter 5 using pretrained embeddings learning continous bag of words embeddings cbow transfer learning using pre trained embeddings chapter 6 sequence modeling for nlp https github com joosthub pytorchnlpbook tree master chapters chapter 6 a sequence representation for surnames chapter 7 intermediate sequence modeling for nlp https github com joosthub pytorchnlpbook tree master chapters chapter 7 generating novel surnames from sequence representations uncondition generation conditioned generation chapter 8 advanced sequence modeling for nlp https github com joosthub pytorchnlpbook tree master chapters chapter 8 understanding packedsequences sequence to sequence learning attention neural machine translation chapter 9 classics frontiers next steps te | natural-language-processing nlp pytorch-nlp pytorch pytorch-tutorial deep-learning deep-neural-networks neural-networks neural-machine-translation | ai |
zhihu | https img shields io badge liscense by 20nelsonzhao brightgreen svg https img shields io badge programming 20language python orange svg repo https zhuanlan zhihu com zhaoyeyu tensorflow 1 anna lstm rnn lstm tensorflow lstm https zhuanlan zhihu com p 27087310 2 skip gram skip gram word2vec tensorflow skip gram https zhuanlan zhihu com p 27296712 3 generate lyrics rnn 4 basic seq2seq rnn encoder decoder seq2seq encoder decoder seq2seq https zhuanlan zhihu com p 27608348 5 denoise auto encoder mnist https zhuanlan zhihu com p 27902193 https raw githubusercontent com nelsonzhao zhihu master denoise auto encoder dae example png 6 cifar cnn kaggle cifar knn cifar https zhuanlan zhihu com p 28035475 https raw githubusercontent com nelsonzhao zhihu master cifar cnn example pic png 7 mnist gan mnist leaky relu gan mnist https zhuanlan zhihu com p 28057434 https raw githubusercontent com nelsonzhao zhihu master mnist gan gan examples png 8 dcgan mnist dcgan batch normalization gan https zhuanlan zhihu com p 28329335 https raw githubusercontent com nelsonzhao zhihu master dcgan mnist dcgan pic png cifar dcgan https raw githubusercontent com nelsonzhao zhihu master dcgan cifar dcgan pic png 9 batch normalization discussion mnist bn tensorflow batch normalization batch normalization https zhuanlan zhihu com p 34879333 https raw githubusercontent com nelsonzhao zhihu master batch normalization discussion bn example png 10 machine translation seq2seq tensorflow 1 6 seq2seq tensorflow seq2seq https zhuanlan zhihu com p 37148308 https raw githubusercontent com nelsonzhao zhihu master machine translation seq2seq pic png 11 mt attention birnn keras seq2seq attention birnn attention attention bleu 0 9 keras attention birnn https zhuanlan zhihu com p 37290775 https raw githubusercontent com nelsonzhao zhihu master mt attention birnn pic1 png https raw githubusercontent com nelsonzhao zhihu master mt attention birnn pic2 png 12 sentiment analysis tensorflow 1 6 dnn lstm cnn sentiment analysis dnn lstm text cnn https zhuanlan zhihu com p 37978321 https raw githubusercontent com nelsonzhao zhihu master sentiment analysis images cnn png 13 image style transfer tensorflow 1 6 image style transfer tensorflow https zhuanlan zhihu com p 38315161 https raw githubusercontent com nelsonzhao zhihu master image style transfer images figures png https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel starry night gif https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel scream gif https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel guernica gif https raw githubusercontent com nelsonzhao zhihu master image style transfer images marvel pattern gif 14 ctr models tensorflow 2 0 deepfm deep cross xdeepfm autoint ctr deepfm deep cross xdeepfm autoint https zhuanlan zhihu com p 109933924 https raw githubusercontent com nelsonzhao zhihu master ctr models pictures ctr models png star fork | deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translation | ai |
LLMs-Planning | llms and planning this repo has the code for two papers 1 the code in plan bench subdirectory belongs to the paper planbench an extensible benchmark for evaluating large language models on planning and reasoning about change plan bench 2 the code in llm planning analysis subdirectory belongs to the paper on the planning abilities of large language models a critical investigation https arxiv org abs 2305 15771 | automated-planning large-language-models planning | ai |
ComputerVision-LocalFeatures | computervision localfeatures feature detection and matching are an essential component of many computer vision applications it is used for variety of applications for example aligning the two images so that they can be seamlessly stitched into a composite mosaics or establishing a dense set of correspondences so that a 3d model can be constructed but selecting the features to compare and match the images is a still ongoing search area there have been many algorithms implemented for this purpose in this assignment we try to implement and review some algorithms like harris mser sift pca sift gloh and then we test our algorithms in the symfeat dataset that contains image pairs exhibiting a range of dramatic variations in lighting age and rendering style | harris mser sift pca-sift gloh computer-vision local-features | ai |
NLP | nlp natural language processing nlp refers to the branch of computer science and more specifically the branch of artificial intelligence or ai concerned with giving computers the ability to understand text and spoken words in much the same way human beings can nlp combines computational linguistics rule based modeling of human language with statistical machine learning and deep learning models together these technologies enable computers to process human language in the form of text or voice data and to understand its full meaning complete with the speaker or writer s intent and sentiment nlp drives computer programs that translate text from one language to another respond to spoken commands and summarize large volumes of text rapidly even in real time there s a good chance you ve interacted with nlp in the form of voice operated gps systems digital assistants speech to text dictation software customer service chatbots and other consumer conveniences but nlp also plays a growing role in enterprise solutions that help streamline business operations increase employee productivity and simplify mission critical business processes | hacktoberfest2021 hacktoberfest-accepted hacktoberfest | ai |
Data-Engineer-Test | data engineer test demonstrating data engineering skill in sakila database at mysql website make new star scheme database based on sakila database and airflow dags that run daily original sakila database err diagram origin sakila err diagram https github com jauhar hakim data engineer test assets 71159391 90e5ed19 1b9b 4cfa bac2 5ebfeccba8c4 new star schema sakila databse err diagram star sakila err diagram https github com jauhar hakim data engineer test assets 71159391 64b10cec ab0b 4831 80aa 1df8b8fa5c33 file property script transforming sakila to star scheme databases sql sql queries script to transform original sakila database to new star scheme sakila database for the first time and one time only script transforming sakila to star scheme databases ipynb sql queries script to transform original sakila database to new star scheme sakila database for the first time and one time only in ipynb file to make more human readable airflow script transform daily py python script that using airflow dags to make script that can run daily with flow to transform new data from old database sakila to new database star scheme sakila | server |
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FreeRTOS-CLI | freertos cli author jungle creat time 2018 3 26 mail yjxxx168 qq com last change 2018 3 26 keil 5 20 stm32f405zet6 freertos 10 0 0 12mhz br securecrt 115200 br br br br cli br cli br freertos cli br more cli freertos cli cli br freertos cli help br br printf br dma br code app notice br 1 usart c line 127 129 br dma cmd disable must disable 2 common h line 28 37 bit field baidu or google br if you need to add one bit please reduce one bit on other br c 3 about submenu please refer to the template in the file cli register c br cli 4 the critical area problem is not considered in the program and it is not recommended to use in interrupts and high frequency is not recommended 5 provide the program under the ucos system ucos iii 6 use caution if you don t do a lot of testing https github com jungleeee freertos cli 1 freertos cli https www freertos org freertos plus freertos plus cli download freertos plus cli shtml | os |
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FullStack_Info_HW04 | fullstack info hw04 full stack web development hw03 advanced javascript the goal of this assignment is to continue to build your javascript jquery skills for this assignment we are going to recreate some of the functionality of music streaming site soundcloud your task is to build a web page that supports the following interactions be sure to read through all of the required interactions before beginning use the startercode hw4 skeleton js file for the two functions listed in the instructions this file can be found in the javascript folder of the github repo you will also be using the stratus library which is a jquery powered soundcloud player include the library using the cdn https stratus soundcloud com stratus js links to an external site links to an external site make sure to also include jquery 1 7 the two functions in startercode hw4 skeleton js includes all the syntax and query parameters that you require if interested you may find concise documentation here stratus https stratus soundcloud com links to an external site links to an external site soundcloud https developers soundcloud com docs api reference tracks links to an external site links to an external site styling is not the primary focus for this assignment so don t worry about making your page beautiful of course you can if you want you re more than welcome to use any css framework requirements the things for which you ll be graded querying soundcloud s api 9 total points 2 point user can input text to search soundcloud s api 1 point use the provided callapi function hint you ll need to pass in the user s search query as the argument for the callapi function 6 points the first 20 results from the response object are rendered on the page each song result displays the following information for that song song title artist picture playing songs via the stratus plugin 5 total points 3 points user can click a play button attached to each song result to play that song via the stratus player plugin 2 points use the provided changetrack function and pass in the song s permalink url as the argument for changetrack hint find the permalink url field for each song in the soundcloud api response object and pass this url as the argument for the changetrack function cut and paste the following link to the stratus player js file in your html file script type text javascript src stratus player js script creating a playlist 12 total points user can create a playlist of songs by picking individual songs from the search results 2 points user can click an add to playlist button attached to each song result to add a song to the playlist 2 point songs are added to the top of the playlist 2 point songs are not removed from the search results when they re added to the playlist hint use the jquery clone method to prevent them from being removed from the search results 2 point user can remove songs from the playlist hint use the jquery remove method 2 points user can move songs up or down in the playlist one spot at a time hint use the jquery prev next insertbefore and insertafter methods 2 point user can play songs that are in the playlist this play functionality is the same as the play functionality in the search results coding style 4 total points 1 point your site has a title and intuitive labeling or instructions on how to use it 1 point your js and css code is separate from your html i e there should be no inline css styling or javascript within your html file 1 point all of your javascript is commented appropriately 1 point your code is clean and concise minimal repetition and unused code bonus you can get up to 4 extra points for doing both of these 2 points related songs when a user plays a song a set of related songs are loaded into a new section of the page these related songs are found via taking the first tag from the tag list field found in each song in the soundcloud api response object and then querying the soundcloud api using this tag as the q attribute of the data object of the get method these related songs can be played or added to the playlist just like how the regular search results the related songs section is loaded independently from the main search results 2 points infinite scroll use a jquery library of your choosing to load the next 20 results from the soundcloud api response object each time the user scrolls to the bottom of the main results there will only be 200 total results so don t be alarmed if the infinite scroll stops after 10 additional result loads | front_end |
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TreePLE | treeple project 06 for instructions and information see the wiki https github mcgill ca ecse321 2018 winter project 06 wiki web application url http ecse321 6 ece mcgill ca 8087 | server |
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TIES483 | nonlinear optimization course university of jyv skyl lecturer markus hartikainen phd these are the materials from nonlinear optimization course taught at the university of jyv skyl finland jan march 2015 course plan introduction 1 first hour basic information about the course and study methods python jupyter etc second hour a very fast introduction to python and jupyter using robert johansson s scienfic python lectures https github com jrjohansson scientific python lectures 2 the very basics what is an optimization problem how to solve one and line search unconstrained optimization 3 direct search methods coordinate descent hooke jeeves and powell s methods 4 steepest descent and newton s method for unrestricted optimization 5 example of using available software scipy optimize constrained optimization 6 optimality conditions 7 indirect methods for constrained optimization 8 direct methods for constrained optimization part 1 9 direct methods for constrained optimization part 2 multiobjective optimization 10 what is multiobjective optimization 11 how to solve multiobjective optimization problems applications of optimization 12 applications of optimization part 1 13 applications of optimization part 2 optimization software 14 algebraic modelling languages especially pyomo 15 ind nimbus r wrapping up 16 how to find and read scientific papers in the field 17 further topics and current research in optimization 18 review study methods python and jupyter this course is taught using jupyter notebooks http ipython org notebook html formerly known as ipython notebooks github the source code of the jupyter notebooks thought in this course will be available at github at https github com maeehart ties483 livereveal the jupyter notebooks will be transferred to slides using https github com damianavila rise to view the slides at home you do not need to have this installed license this work is licensed under a creative commons attribution 3 0 unported license http creativecommons org licenses by 3 0 | server |
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