uvadlc_notebooks
jaxrl
uvadlc_notebooks | jaxrl | |
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2 | 2 | |
2,163 | 576 | |
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6.6 | 0.0 | |
17 days ago | over 1 year ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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uvadlc_notebooks
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I miss the old days where people asked me to recreate “Facebook” or “Twitter”
So, I don’t have anything simple that’s readily available, and I don’t know how much you’d get from the code itself without some background. But I would recommend the UVA Deep Learning tutorials. Particularly, I’d recommend trying the autoencoder as a good start (tutorial 9). Autoencoders are very easy and fast models to train.
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[D] Favorite Colab Notebooks / runnable tutorials on adversarial CV
DL course from the University of Amsterdam:Github and Colab including another FGSM example
jaxrl
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JAX in Reinforcement Learning
Have you looked at this repo or this repo ?
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CleanRL now has a DDPG + JAX implementation roughly 2.5-4x faster than DDPG + PyTorch
https://github.com/ikostrikov/jaxrl would be another great reference implementation. Probably you want to also checkout the docs for jax, flax, and optax.
What are some alternatives?
tensor-sensor - The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.
jaxrl_m - Skeleton for scalable and flexible Jax RL implementations
hyper-nn - Easy Hypernetworks in Pytorch and Jax
sbx - SBX: Stable Baselines Jax (SB3 + Jax)
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
indaba-pracs-2022 - Notebooks for the Practicals at the Deep Learning Indaba 2022.
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
TF_JAX_tutorials - All about the fundamental blocks of TF and JAX!
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱