This repository provides a comprehensive learning path and knowledge summary for machine learning and deep learning, targeting individuals seeking to understand and apply AI technologies. It offers a structured approach to mastering foundational mathematics, Python libraries, core ML algorithms, advanced deep learning models, and reinforcement learning, culminating in practical project applications and research paper analysis.

How It Works

The project is structured as a curated collection of notes and resources, organized by learning progression. It emphasizes a systematic approach, starting with essential mathematical foundations (calculus, linear algebra) and Python programming with key libraries (NumPy, Pandas, Scikit-Learn). It then delves into traditional machine learning algorithms and progresses to deep learning concepts, including neural networks, CNNs, RNNs, GANs, and Transformers, with a strong recommendation for PyTorch.

Quick Start & Requirements

• Installation: Primarily relies on Python and its associated libraries. No specific installation command is provided, but the content suggests using Anaconda for environment management and VS Code or PyCharm for IDEs.
• Prerequisites: Python, NumPy, Pandas, Matplotlib, Scikit-Learn. For deep learning, PyTorch is highly recommended. Access to online resources like Coursera courses and platforms like Kaggle is also suggested.
• Resources: Links to Coursera courses, official PyTorch tutorials, and GitHub repositories for paper implementations are provided.

Highlighted Details

• Covers a wide range of ML/DL topics from foundational math to advanced LLMs and multimodal models.
• Strong emphasis on PyTorch as the preferred deep learning framework.
• Includes practical project examples and guidance on participating in competitions like Kaggle.
• Curated list of recommended books and online learning resources.
• Dedicated sections on Large Language Models (LLMs) and multimodal AI, including recent advancements.

Maintenance & Community

The repository is marked as "持续更新中" (continuously updated). The author welcomes feedback via GitHub Issues or Pull Requests and encourages collaboration. Specific contributors or community channels (like Discord/Slack) are not explicitly mentioned.

Licensing & Compatibility

The repository itself does not specify a license. The content references various external resources, whose licenses would apply independently.

Limitations & Caveats

Some sections are marked as "TBD" (To be done), indicating incomplete content. The author notes personal energy limitations and welcomes community contributions to fill these gaps. The project is a personal learning summary and not a formal course or software library.