Both sides previous revisionPrevious revisionNext revision | Previous revision |
learning:intro [2019/11/29 17:46] – orel | learning:intro [2024/03/18 15:06] (current) – external edit 127.0.0.1 |
---|
| |
* http://ai.berkeley.edu/lecture_slides.html | * http://ai.berkeley.edu/lecture_slides.html |
| * https://github.com/janishar/mit-deep-learning-book-pdf (MIT Book) |
| * https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf (RL) |
| |
=== Intro à TensorFlow === | === Intro à TensorFlow === |
* https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ | * https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ |
* https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/ | * https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/ |
* https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/ | * https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/ |
* https://www.youtube.com/watch?v=O9yl9KKKoQI | * https://www.youtube.com/watch?v=O9yl9KKKoQI |
* Keras : https://www.tensorflow.org/guide/keras/overview (pip3 install keras) | * Keras : https://www.tensorflow.org/guide/keras/overview (pip3 install keras) |
* Gym (game simulator) : https://gym.openai.com/ (pip3 install gym atari_py) | * Gym (game simulator) : https://gym.openai.com/ (pip3 install gym atari_py) |
* [[https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb|quick start colab]] | * [[https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb|quick start colab]] |
* Q Learning avec Keras+Gym : https://keon.io/deep-q-learning/ | * Deep Q Learning (DQN) avec Keras+Gym : Cart Pole Sample |
| * https://keon.io/deep-q-learning/ |
| * https://medium.com/@gtnjuvin/my-journey-into-deep-q-learning-with-keras-and-gym-3e779cc12762 |
| |
| === Atari 2600 === |
| |
| * Playing Atari with Deep Reinforcement Learning, Deepmind.com (2013). |
| * https://arxiv.org/pdf/1312.5602.pdf |
| * Learning from the memory of Atari 2600 (2016). |
| * https://deepsense.ai/playing-atari-on-ram-with-deep-q-learning/ |
| * https://github.com/sygi/deep_q_rl |
| * https://cdn-sv1.deepsense.ai/wp-content/uploads/2016/09/1605.01335v1-4.pdf |
| * DQN : Atari Breakout examples |
| * https://becominghuman.ai/lets-build-an-atari-ai-part-1-dqn-df57e8ff3b26 |
| * https://github.com/rbrigden/breakout-ram-dqn |
| * https://towardsdatascience.com/tutorial-double-deep-q-learning-with-dueling-network-architectures-4c1b3fb7f756 |
| * https://github.com/andi611/DQN-Deep-Q-Network-Atari-Breakout-Tensorflow |
| * https://deepsense.ai/playing-atari-on-ram-with-deep-q-learning/ |
| * ... |
| * Reinforcement Learning + Keras + Atari : https://github.com/rlcode/reinforcement-learning |
| * DDQN + Genetic |
| * https://towardsdatascience.com/atari-reinforcement-learning-in-depth-part-1-ddqn-ceaa762a546f |
| * https://towardsdatascience.com/atari-solving-games-with-ai-part-2-neuroevolution-aac2ebb6c72b |
| |
| === About Graph === |
| |
| * Graph Representation Learning: https://www.youtube.com/watch?v=YrhBZUtgG4E |
| * GCN : https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780 |
| * https://rlgm.github.io/papers/41.pdf |
| * Semi-Supervised Classification with Graph Convolutional Networks: https://arxiv.org/abs/1609.02907 |
| |
=== Fun! === | === Fun! === |
| |
* https://github.com/greerviau/SnakeAI | * https://github.com/greerviau/SnakeAI + https://www.youtube.com/watch?v=zIkBYwdkuTk |
* Alpha Toe | * Alpha Toe |
* https://github.com/DanielSlater/AlphaToe | * https://github.com/DanielSlater/AlphaToe |