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New Book: Practical GenAI and Machine Learning – Projects and Datasets

This book is for participants in my AI and machine learning certification program. However, it is now free and available to everyone. With tutorials, enterprise-grade projects and solutions, it covers state-of-the-art material on topics such as generative adversarial networks (GAN), specialized LLM, data synthetization, as well as classical machine learning. It is a work in progress, as I regularly add new projects and new techniques.

The focus is on fast, simple, and better methods, by comparison to vendor solutions. For instance: NoGAN, better evaluation metrics, xLLM (specialized multi-LLM with taxonomies and self-tuning), variable-length embeddings, generating observations outside the training set range, or fast probabilistic vector search. In classical ML, I discuss simple techniques, for instance to synthesize geo-spatial data. Also, I show how to increase speed, size and quality without neural networks.

This textbook is an invaluable resource to instructors and professors teaching AI, or for corporate training. Also, it is useful to prepare for job interviews or to build a robust portfolio. And for hiring managers, there are plenty of original interview questions. The amount of Python code accompanying the solutions is considerable, using a vast array of libraries as well as home-made implementations showing the inner workings and improving existing black-box algorithms. By itself, this book constitutes a solid introduction to Python and scientific programming. The code is also on my GitHub repository.

The first three steps in the main xLLM project


  • Machine learning optimization (including NoGAN, automated SQL)
  • Time series and spatial processes
  • Scientific computing (including synthetic music)
  • Generative AI (with explainable AI)
  • Data visualizations and animations
  • NLP and large language models
  • Glossary: GAN and synthetic data
  • Glossary: GenAI and LLMs
  • Introduction to extreme LLM (xLLM) and GPT
  • Bibliography
  • Index

How to Get Your Copy?

The 158-page textbook is free. You can read or download it on GitHub, here. The Python code is also on GitHub, in the same repository. To not miss future updates about the book (I have more projects to add or finish) sign-up to my newsletter, here. Upon signing-up, you will get a code to access member-only content. There is no cost. The same code gives you a 20% discount on all my eBooks in my eStore, here.


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Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist at MLTechniques.com and GenAItechLab.com, former VC-funded executive, author (Elsevier) and patent owner — one related to LLM. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Follow Vincent on LinkedIn.