ai_book
AI book for everyone (by YeonwooSung)
RL-Adventure-2
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters [Moved to: https://github.com/higgsfield-ai/higgsfield] (by higgsfield)
ai_book | RL-Adventure-2 | |
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1 | 3 | |
19 | 3,211 | |
- | - | |
8.7 | 0.0 | |
8 days ago | 5 months ago | |
Jupyter Notebook | Jupyter Notebook | |
- | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ai_book
Posts with mentions or reviews of ai_book.
We have used some of these posts to build our list of alternatives
and similar projects.
RL-Adventure-2
Posts with mentions or reviews of RL-Adventure-2.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-19.
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Why does this implementation uniformly initialize the final layer off their network
I am following this implementation of ddpg and found this code -
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RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed)
Also, modified this code - https://github.com/higgsfield/RL-Adventure-2/blob/master/1.actor-critic.ipynb
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[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples
Lecture 8: Policy gradient methods. (slides, code, theory, video)
What are some alternatives?
When comparing ai_book and RL-Adventure-2 you can also consider the following projects:
biggestwar_ai
RL-Adventure - Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
pytorch-serving-workshop - Slides and notebook for the workshop on serving bert models in production
flux-beamer - Flux is a modern style beamer presentation.
DeepReinforcementLearningInAction - Code from the Deep Reinforcement Learning in Action book from Manning, Inc
implementation-matters