FunMatch-Distillation
Practical_RL
| FunMatch-Distillation | Practical_RL | |
|---|---|---|
| 1 | 2 | |
| 79 | 5,753 | |
| - | 1.1% | |
| 1.8 | 6.0 | |
| over 2 years ago | 24 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | The Unlicense |
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FunMatch-Distillation
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Knowledge distillation with "Function Matching"
There's a notebook on distributed hyperparameter tuning and it's often not included in the public release of an implementation.
Practical_RL
- [D] implementation of MCTS in Python
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Alternatives to OpenAI’s spinning up?
there is this great github repo where there are lectures and other resources, and have a week by week jupyter notebooks where they explain and code with homeworks at the very end of it. is basics and deepRL, but just dqn and DDPG/ppo but i think will give you good start in the topic for later star working on your own.
What are some alternatives?
poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
webdataset - A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
awesome-rl - Reinforcement learning resources curated
PAWS-TF - Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
adanet - Fast and flexible AutoML with learning guarantees.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.
redisai-examples - RedisAI showcase
TensorFlow-Tutorials - TensorFlow Tutorials with YouTube Videos
YPDL-Build-a-movie-recommendation-engine-with-TensorFlow - In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.