Learn how to develop, deploy and iterate on production-grade ML applications.
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1–11 of 11 · Most stars
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
- machine-learning
- trading
- investment
- finance
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
- computer-vision
- deep-learning
- drug-discovery
- forecasting
TranslatedChinese tutorial on reinforcement learning (Mushroom Book 🍄), read online at: https://datawhalechina.github.io/easy-rl/
- deep-reinforcement-learning
- reinforcement-learning
- dqn
- ppo
Python training for business analysts and traders
- python
- jupyter
- jupyterlab
- binder
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
- pytorch
- pytorch-tutorials
- pytorch-tutorials-cn
- deep-learning
Quantitative analysis, strategies and backtests
- quantitative-finance
- quantitative-trading
- portfolio-management
- risk-management
Mostly experiments based on "Advances in financial machine learning" book
💸 Papers and Code Implements for Quantitative-Trading
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.