CodeSearchNet
dopamine
Our great sponsors
| CodeSearchNet | dopamine | |
|---|---|---|
| 2 | 3 | |
| 1,904 | 10,367 | |
| - | 0.4% | |
| 0.0 | 4.8 | |
| about 2 years ago | 22 days 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.
CodeSearchNet
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Fine tuning
The CodeSearchNet challenge provides a dataset of code documentation comments, along with pre-trained models and fine-tuning scripts. You can find the challenge and resources at https://github.com/github/CodeSearchNet.
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Speedtyper.dev: Type racing for programmers
https://github.com/github/CodeSearchNet#downloading-data-from-s3
dopamine
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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.
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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?
AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
data - Data and code behind the articles and graphics at FiveThirtyEight
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
awesome-speech-recognition-speech-synthesis-papers - Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
airline-sentiment-streaming - Streaming with Airline Sentiment. Utilizing Cloudera Machine Learning, Apache NiFi, Apache Hue, Apache Impala, Apache Kudu
pycaret - An open-source, low-code machine learning library in Python
nlpaug - Data augmentation for NLP
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
ai-traineree - PyTorch agents and tools for (Deep) Reinforcement Learning
trulens - Evaluation and Tracking for LLM Experiments
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)