Note
quickai
| Note | quickai | |
|---|---|---|
| 48 | 7 | |
| 35 | 162 | |
| - | - | |
| 9.9 | 3.7 | |
| 3 days ago | about 1 month ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
Note
- Easily implement parallel training.
- This project allows you to easily implement parallel training with the multiprocessing module.
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Train neural networks in parallel using Python's multiprocessing module.
https://github.com/NoteDancing/Note This project allows you to train neural network in parallel using Python's multiprocessing module.
- A system for deep learning and reinforcement learning.
- A system for deep learning and reinforcement learning. (r/MachineLearning)
- [P] A system for deep learning and reinforcement learning.
quickai
- Show HN: QuickAI Version 2 Released
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QuickAI version 2 released!
I originally released QuickAI here. I am very excited to announce version 2 of QuickAI
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QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
GitHub: https://github.com/geekjr/quickai
- Show HN: Quickai – Quickly experiment with state-of-the-art ML models
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quickai - A Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
Yeah, totally agree. https://github.com/geekjr/quickai/blob/main/quickai/image_classification.py does really need some reworking. Dicts are the way to go. But once that's done, I think it could actually be a practical lib!
What are some alternatives?
deep-RL-trading - playing idealized trading games with deep reinforcement learning
detoxify - Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].
deep-significance - Enabling easy statistical significance testing for deep neural networks.
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
softlearning - Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
segyio - Fast Python library for SEGY files.
muzero-general - MuZero
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
chappie.ai - Generalized AI to perform a multitude of tasks written in python3
neptune-contrib - This library is a location of the LegacyLogger for PyTorch Lightning.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.