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Differential geometry is a mathematical discipline that studies smooth manifolds, using techniques of vector calculus, linear algebra & multilinear algebra. It was used by Einstein in his development of General Relativity: en.wikipedia.org/wiki/Differ… Book: link.amazon/B01Y1kXgY

Are small language models the future? Some AI leaders say “yes”. "How to Build and Fine‐Tune a Small Language Model: A Step-by-Step Guide for Beginners, Researchers, and Non-Programmers" Available at amzn.to/4aV0amA Build your own AI—without a PhD, expensive hardware, or industry-level resources. Whether you’re a beginner, a student, a scientist, or a domain expert, this book shows you how to create, train, fine-tune, and deploy Small Language Models (SLMs) that truly understand your field. Most AI books explain what models are. This one teaches you to build them. You’ll go from zero to a working GPT-style model, then learn how to fine-tune, align, evaluate, and deploy it for real applications. All chapters include ready-to-run Google Colab notebooks.

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AI in UX Design — How UX Designers are Using AI in the Age of Artificial Intelligence: link.amazon/B02qtkLg8 from @PacktPublishing Amazon Summary: “Move beyond AI hype and learn how modern UX designers use ChatGPT, Claude, Figma, Lovable, Replit, and no-code tools to turn insight into tested experiences while keeping judgment, ethics, and users at the center.” Key Features: • Discover how AI shifts UX from static deliverables to active product orchestration • Apply prompts for briefs, personas, journeys, requirements, and usability planning • Prototype earlier with no-code AI tools and reduce handoff risk before build

Designing Information Architecture — Practical guide to structuring digital content for findability and easy navigability: amzn.to/4xkhGK3 (570 pages) from @PacktPublishing About the Author: Pabini Gabriel-Petit is the founder, publisher, and editor-in-chief of UXMatters. With more than 20 years working in User Experience at companies such as Google, Cisco, WebEx, and Apple, among others, Pabini now provides UX strategy and design consulting services through her Silicon Valley company, Strategic UX. She is passionate about creating great user experiences that meet users' needs and get business results. A thought leader in the UX community, Pabini was a Founding Director of the Interaction Design Association (IxDA).

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Before Machine Learning [Volume 3] - Probability & Statistics for AI: The Fundamental Mathematics for Data Science and AI [400 pages] Get it here: amzn.to/4vda8qT Also... Vol. 1 - Linear Algebra: amzn.to/3QBnHlM Vol. 2 - Calculus: amzn.to/4gQd4WD Now ML - Supervised Learning: amzn.to/3SCkqTP

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Probability and Statistics for Computer Scientists — Free PDF

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Comprehensive “Deep Learning” textbook Read individual chapters for free online: deeplearningbook.org or buy the full 800-page masterpiece here: amzn.to/3T6nKXx Amazon summary: “The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.”

"Vector: A Surprising Story of Space, Time, and Mathematical Transformation" Find it here: link.amazon/B01fj3ZFX Amazon description: A celebration of the seemingly simple idea that allowed us to imagine the world in new dimensions—sparking both controversy and discovery.  Vector and tensor calculus offers an elegant language for expressing the way things behave in space and time, and Robyn Arianrhod shows how this enabled physicists and mathematicians to think in a brand-new way. These include James Clerk Maxwell when he ushered in the wireless electromagnetic age; Einstein when he predicted the curving of space-time and the existence of gravitational waves; Paul Dirac, when he created quantum field theory; and Emmy Noether, when she connected mathematical symmetry and the conservation of energy. It turned out that it’s not just physical quantities and dimensions that vectors and tensors can represent, but other dimensions and other kinds of information, too. This is why physicists and mathematicians can speak of four-dimensional space-time and other higher-dimensional “spaces,” and why you’re likely relying on vectors or tensors whenever you use digital applications such as search engines, GPS, or your mobile phone.