This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms.
The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing large language models for text analysis.
Coverage extends across several core capability areas, including computer vision development for object recognition and synthetic media generation, and financial engineering for portfolio optimization and algorithmic trading. The project also encompasses predictive model development for classification and regression tasks, as well as probabilistic frameworks for A/B testing and uncertainty quantification.
The examples are implemented in Python and include configurations for GPU environments on Linux.
