update readme with Hugging Face config

README.md

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+---
+title: Sudoku Deeplearning 100% accurate Sudoku solving with deep learning algorithm
+emoji: 🖊️🎲🎯
+colorFrom: blue
+colorTo: purple
+sdk: streamlit
+sdk_version: "1.41.0"
+app_file: app.py
+pinned: true
+---

Readme.md

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-## metrics to track
-- loss per epoch per model boost layer
-- number of error per epoch model boost layer
-- number of resolved puzzles per epochs
-- threshold per epochs per model layer
-- number of filled digits per model boost layer per epoch for both pis ans abs
-
-## TODO
-- jupyter notebook to python file
-- threshold compute on test set (with adding a gap) each epoch. and training threshold initialised with test thresholds that evolve each error during training.
-
-## Possible way
-- it might be smart to store the intermitent states as boost layereds "buffers". at the end the first X go to the model layer 0 let write it as puseudo code
-
-### Method threshold
-```
-global init
-th ->  -10
-training step
-  init 
-    pass
-  training loop
-    keep th behind the error limit
-validation step
-  init
-    compute_th =-10
-  validation loop
-    keep compute_th behind error limit + marge
-    but use th
-  end
-    th= compute_th
-```
-
-### Method training
-```
-Xs -> the x initial batch vector 
-Y -> the y batch vector
-Xs' = M0(Xs)
-then we filter Xs'=Y -> resolved sudokus
-Xs'==Xs -> we add the rows to X1 buffer
-and the remaning Xs' is added to X0 buffer.
-```
-
-then we look at each buffers X0 to Xn and we process each of them that are => batch size.
-
-When every buffer are smaller than batch size the process is finished.
-
-object 
-```
-Buffers
-get_batch(limit_batch_size=True) -> idx, Xb # Xb could be none. (Xb should be a shuffled sample of the batch)
-add_batch(Xp, idx)
-```
-
-
-### Loss optimisation
-Both 0 and 1 target are different in the way we should gradient descend them.
-y==0 point is something easy: it should be as low as possible I thing we can use the usual log loss function on it.
-y==1 is different: there is different case possible:
- - the point could be "unpredictable" in that case the gradient descend should be tuned to low, we expect the predictive function to have a low score.
- - the point could be well predicted in that case we hope the value is prety hight and we would like to the the gradient descend more heavely.
-This could be applied by using a sigmoid centered on the threshold
-
-
-### Paper writing
-
-Les niveaux supérieurs font appel à divers types de chaînes :
-
-11.6 Dynamic + Dynamic Forcing Chains (145-192 nodes) Cell Forcing Chains
-11.7 Dynamic + Dynamic Forcing Chains (193-288 nodes) Double Forcing Chains
-Ces Dynamic Forcing Chains sont une forme d’essais et erreurs.
-
-### Trial and error solving technique
-We applied trial and error solving technique to reach 100% accuracy over sudoku. The resoning is simple we find the best digit/position to test and produce 2 children grid one with the number the other without. the we process each grid until one of them break sudoku's rules .
-
-The V1 of this algorithm should only stopped at 1 trail and error test (no binary tree search) it should be simpler and feasible and if not : we will se an improve and try the next step.
-