Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Numpy Ml | 14,162 | 5 months ago | 3 | June 20, 2020 | 35 | gpl-3.0 | Python | |||
Machine learning, in numpy | ||||||||||
Personae | 1,034 | 5 years ago | 8 | mit | Python | |||||
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. | ||||||||||
Tensorflow Tutorial | 751 | 6 years ago | ||||||||
TensorFlow and Deep Learning Tutorials | ||||||||||
Async_deep_reinforce | 560 | 6 years ago | 34 | apache-2.0 | Python | |||||
Asynchronous Methods for Deep Reinforcement Learning | ||||||||||
Async Rl | 372 | 7 years ago | 20 | mit | Python | |||||
Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783) | ||||||||||
Papers For Molecular Design Using Dl | 367 | 2 months ago | gpl-3.0 | |||||||
List of molecular design using Generative AI and Deep Learning | ||||||||||
Accel Brain Code | 289 | 2 | 3 months ago | 12 | July 26, 2022 | 1 | gpl-2.0 | Python | ||
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing. | ||||||||||
Deeprl Grounding | 213 | 6 years ago | 1 | mit | Python | |||||
Train an RL agent to execute natural language instructions in a 3D Environment (PyTorch) | ||||||||||
Spirl | 154 | 10 months ago | 4 | Python | ||||||
Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020 | ||||||||||
Deep Algotrading | 145 | 6 years ago | mit | Jupyter Notebook | ||||||
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading |