TensorFlow-Tutorials
Practical_RL
TensorFlow-Tutorials | Practical_RL | |
---|---|---|
2 | 2 | |
9,250 | 5,716 | |
- | 0.5% | |
0.0 | 6.0 | |
over 3 years ago | 21 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | The Unlicense |
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TensorFlow-Tutorials
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Probabilistic forecasting
"deep neural network" https://github.com/Hvass-Labs/TensorFlow-Tutorials
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Plagiarism is just bad
The majority of this code is taken from the TensorFlow-Tutorials. I highly recommend them to those who want to get started with TensorFlow.
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?
car-damage-detection - Detectron2 for car damage detection using custom dataset
webdataset - A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
YOLO_Object_Detection - This is the code for "YOLO Object Detection" by Siraj Raval on Youtube
FunMatch-Distillation - TF2 implementation of knowledge distillation using the "function matching" hypothesis from https://arxiv.org/abs/2106.05237.
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
awesome-rl - Reinforcement learning resources curated
m1-machine-learning-test - Code for testing various M1 Chip benchmarks with TensorFlow.
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
redisai-examples - RedisAI showcase