https://huggingface.co/

https://github.com/karpathy/nanoGPT

https://ai.plainenglish.io/reinforcement-learning-is-dead-long-live-the-transformer-228835689841

https://github.com/kzl/decision-transformer/tree/master

stormdrop : génération de nombre aléatoires

https://github.com/williamstaffordparsons

https://www.futura-sciences.com/tech/actualites/technologie-elon-musk-predit-ia-va-heurter-cette-double-penurie-an-112175/

robotics

https://www.bostondynamics.com/

habana labs

https://techcrunch.com/2019/12/16/intel-buys-ai-chipmaker-habana-for-2-billion/

reconnaissance d'images

https://github.com/facebookresearch/segment-anything

from text to image

https://huggingface.co/spaces/stabilityai/stable-diffusion

Social

https://hackernoon.com/

Docs

https://en.wikipedia.org/wiki/Sigmoid_function

https://fr.wikipedia.org/wiki/Q-learning

keras

https://lesdieuxducode.com/blog/2019/1/prototyper-un-reseau-de-neurones-avec-keras

https://keras.io/api/layers/initializers/

https://keras.io/optimizers/

https://keras.io/getting-started/sequential-model-guide/

https://www.tensorflow.org/guide/keras/overview

Migrate from tf1 to tf2

https://www.tensorflow.org/guide/migrate

Practical guides

https://github.com/adventuresinML/adventures-in-ml-code

Practical guides reinforcement

https://blog.tensorflow.org/2018/07/deep-reinforcement-learning-keras-eager-execution.html?m=1

https://github.com/dennybritz/reinforcement-learning

datasets for reinforcement

https://gym.openai.com/docs/

https://github.com/openai/gym/wiki/Leaderboard

https://github.com/openai/gym

Synthetic approaches

http://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/fr_Tanagra_Packages_Python_for_Deep_Learning.pdf

http://www.morrisriedel.de/wp-content/uploads/2018/04/2018-04-19-Deep-Learning-with-Python-Morris-Riedel-v1.pdf

inside tensorflow

https://www.tensorflow.org/guide/keras

articles on reinforcement

https://www.alexirpan.com/2018/02/14/rl-hard.html

https://teahouse.fifty-five.com/fr/petit-guide-du-machine-learning-partie-4-lapprentissage-par-renforcement/

https://blog.engineering.publicissapient.fr/2020/02/28/reinforcement-learning-partie-2-one-step-deeper/

deeplearning.ai and al

TD lambda https://amreis.github.io/ml/reinf-learn/2017/11/02/reinforcement-learning-eligibility-traces.html

http://www.isir.upmc.fr/files/pdmia_chapAR_sigaud_garcia08.pdf

a pdf draft a scribed notes during the course

https://github.com/bhunkeler/DataScienceCoursera/blob/master/Machine_Learning%20-%20Stanford%20University/Lectures/007_Regularization/010_Documentation/7.%20Regularization.pdf

https://github.com/andersy005/deep-learning-specialization-coursera/blob/master/01-Neural-Networks-and-Deep-Learning/week2/02-vectorization.ipynb

with cleaned readme.md notes

https://github.com/Bennyhwanggggg/Coursera-Deep-Learning-Specialisation/tree/master/Improving_Deep_Neural_Networks-Hyperparameter_tuning_Regularization_and_Optimization#dropout-regularization

this is the raw material from the course I think

https://github.com/raymondlam19/Deep-Learning-Specialization/tree/master/2.%20Improving%20Deep%20Neural%20Networks-%20Hyperparameter%20tuning-%20Regularization%20and%20Optimization

https://github.com/enggen/Deep-Learning-Coursera/blob/master/Improving%20Deep%20Neural%20Networks%20Hyperparameter%20tuning%2C%20Regularization%20and%20Optimization/Regularization.ipynb

https://github.com/ageron/handson-ml