This repository provides supplementary materials for the "Machine Learning Q and AI: Beyond the Basics" book. It targets practitioners who have a foundational understanding of ML and AI and wish to deepen their knowledge on specific, advanced topics through a Q&A format. The book aims to clarify lingering knowledge gaps and introduce current technologies for practical implementation.

How It Works

The book is structured as a series of 30 short chapters, each addressing a specific question in machine learning and AI. It employs diagrams for conceptual explanations and provides ample references for further study. The supplementary materials include Python notebooks (.ipynb) for select topics, offering practical code examples for concepts like data sampling, dropout, and various evaluation metrics for LLMs.

Quick Start & Requirements

• Installation: No specific installation instructions are provided for the book content itself. Access is via the README and linked resources.
• Prerequisites: A general understanding of machine learning and AI concepts is assumed. Python notebooks may require a Python environment with libraries like NumPy, Pandas, and potentially ML frameworks (e.g., PyTorch, TensorFlow) depending on the specific notebook's content.
• Links:
  • Preorder from No Starch Press: https://nostarch.com/machine-learning-q-and-ai
  • Preorder from Amazon: https://www.amazon.com/Machine-Learning-AI-Beyond-Basics/dp/1718503423
  • Table of Contents: Provided within the README.

Highlighted Details

• Covers advanced topics such as multi-GPU training, transformer architectures (encoder/decoder differences, vision transformers), self-supervised learning, and few-shot learning.
• Includes practical code examples for data augmentation techniques in NLP and various LLM evaluation metrics (BERTScore, BLEU, ROUGE).
• Addresses crucial concepts in model evaluation, including confidence intervals, conformal predictions, and cross-validation nuances.
• Explores production and deployment considerations like data-centric AI and inference speed optimization.

Maintenance & Community

The project is associated with Sebastian Raschka, a recognized educator in the ML field. Questions regarding the book are directed to the "Discussions" section of the repository.

Licensing & Compatibility

The repository itself does not explicitly state a license. The book is published by No Starch Press, implying standard book publishing terms. Compatibility for commercial use or closed-source linking would depend on the terms set by the publisher and author, which are not detailed in the README.

Limitations & Caveats

The README does not specify the license for the supplementary code or materials. While the book covers advanced topics, the repository primarily serves as a pointer to the book and its preorder links, with limited direct code execution guidance.