Understanding_the_EM_Algorithm
dopamine
| Understanding_the_EM_Algorithm | dopamine | |
|---|---|---|
| 1 | 3 | |
| 7 | 10,375 | |
| - | 0.2% | |
| 0.0 | 4.8 | |
| about 2 years ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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.
Understanding_the_EM_Algorithm
-
[D] My new blog post "Understanding the EM Algorithm"
The EM algorithm is very straightforward to understand with one or two proof-of-concept examples. However, if you really want to understand how it works, it may take a while to walk through the math. The purpose of this article is to establish a good intuition for you, while also provide the mathematical proofs for interested readers. The codes for all the examples mentioned in this article can be found at https://github.com/mistylight/Understanding_the_EM_Algorithm.
dopamine
-
Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
-
RL review
You can also reference the source code for some of the popular implementations from open source RL libraries like stablebaselines3, RLlib, CleanRL, or Dopamine. These can help you if you’re trying to compare your implementation to a “standard”.
- Rainbow Library
What are some alternatives?
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
airline-sentiment-streaming - Streaming with Airline Sentiment. Utilizing Cloudera Machine Learning, Apache NiFi, Apache Hue, Apache Impala, Apache Kudu
nlpaug - Data augmentation for NLP
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
ai-traineree - PyTorch agents and tools for (Deep) Reinforcement Learning
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
creative-prediction - Creative Prediction with Neural Networks
lmgtfy - A "Let Me Google That For You" clone that's open source and doesn't track you when you share it.
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.