This repository is a comprehensive collection of notes and explanations on machine learning and deep learning principles, targeting students and practitioners seeking a deep understanding of both theoretical foundations and practical applications. It aims to demystify complex ML concepts through detailed derivations and code examples, serving as a valuable reference for anyone involved in data science or AI development.

How It Works

The project is structured into three main parts: mathematical foundations (calculus, probability, linear algebra), machine learning algorithms (regression, SVMs, clustering, ensemble methods), and deep learning concepts (DNNs, CNNs, RNNs, NLP, reinforcement learning). It emphasizes detailed formula derivations and covers a wide array of algorithms, often linking them to practical implementations using libraries like NumPy, Scikit-learn, TensorFlow, and Keras.

Quick Start & Requirements

• Installation: Primarily for reference and learning; no direct installation command is provided. Code examples likely require Python and relevant ML libraries.
• Prerequisites: Python, NumPy, Scikit-learn, Pandas, PySpark, TensorFlow, Keras. Specific versions are not mandated but implied by library usage.
• Resources: Requires a standard development environment; no specific hardware like GPUs is explicitly mentioned for the core notes, though deep learning examples might benefit.
• Links: Gitbook: https://shunliz.gitbooks.io/machine-learning/content/

Highlighted Details

• Extensive coverage of mathematical prerequisites for ML.
• Detailed explanations of core ML algorithms like SVM, Decision Trees, and Clustering.
• In-depth exploration of deep learning architectures including CNNs, RNNs, and Transformers.
• Sections dedicated to Natural Language Processing (NLP) and Reinforcement Learning.
• Includes practical library usage examples with TensorFlow, Keras, and Scikit-learn.

Maintenance & Community

The repository has not been updated in the last six months due to the author's personal circumstances. The author mentions receiving a donation that motivated continued updates. There are no explicit community channels or contributor lists provided.

Licensing & Compatibility

The repository's content is stated to be "扒来的" (scraped from the internet) and explicitly mentions potential copyright infringement, inviting contact for removal. No specific open-source license is declared.

Limitations & Caveats

The content is presented as personal notes and may not represent a curated or validated curriculum. The author acknowledges that much of the content is sourced from the internet, raising potential copyright concerns. The project's update frequency has been inconsistent.