| ddbourgin/numpy-ml |
14,162 |
|
0 |
0 |
over 2 years ago |
3 |
June 20, 2020 |
35 |
gpl-3.0 |
Python |
| Machine learning, in numpy |
| Ceruleanacg/Personae |
1,034 |
|
0 |
0 |
over 7 years ago |
0 |
|
8 |
mit |
Python |
| 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. |
| wagamamaz/tensorflow-tutorial |
751 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
|
|
| TensorFlow and Deep Learning Tutorials |
| miyosuda/async_deep_reinforce |
560 |
|
0 |
0 |
over 7 years ago |
0 |
|
34 |
apache-2.0 |
Python |
| Asynchronous Methods for Deep Reinforcement Learning |
| muupan/async-rl |
372 |
|
0 |
0 |
about 9 years ago |
0 |
|
20 |
mit |
Python |
| Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783) |
| AspirinCode/papers-for-molecular-design-using-DL |
367 |
|
0 |
0 |
about 2 years ago |
0 |
|
0 |
gpl-3.0 |
|
| List of molecular design using Generative AI and Deep Learning |
| accel-brain/accel-brain-code |
289 |
|
0 |
2 |
over 2 years 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. |
| devendrachaplot/DeepRL-Grounding |
213 |
|
0 |
0 |
almost 8 years ago |
0 |
|
1 |
mit |
Python |
| Train an RL agent to execute natural language instructions in a 3D Environment (PyTorch) |
| clvrai/spirl |
154 |
|
0 |
0 |
almost 3 years ago |
0 |
|
4 |
|
Python |
| Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020 |
| LiamConnell/deep-algotrading |
145 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
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 |
