Books

A progressive journey from high school mechanics to neural networks. By pushing the humble pendulum beyond the small-angle approximation, discover how classical physics, pure mathematics, Python simulation, and modern AI work together as interconnected lenses.Featuring 100% fully solved exercises and interactive Google Colab notebooks that bridge classical mechanics and Physics-Informed Neural Networks.

What I love about Dask is that it won't make us start from scratch. It doesn't ask us to learn a new language, adopt a new way of thinking, or rewrite five years of careful analysis into something completely different. It just takes the tools we've already got and quietly teaches them to work in parallel. The dataframe remains a dataframe. The array is still an array. The difference is that the work now spreads across eight cores, or eight hundred, and we barely had to change how we think.

Behind every breakthrough in Generative AI isn't magic—it's mathematics."​Ever wondered what actually happens inside a Large Language Model when it generates the next token? Beyond the hype and high-level API calls lies an elegant foundation of probability, vector geometry, and linear algebra.​In The Mathematics of Generative AI, Ahmed Adawy strips away the academic jargon to reveal the exact core equations driving modern LLMs. From conditional probability and high-dimensional embeddings to Query-Key-Value attention mechanisms, this capsule equips software engineers and AI practitioners with the intuition needed to build, debug, and innovate with confidence.​Stop treating AI like a black box. Master the equations beneath the intelligence.

Master NumPy from the ground up and learn to write fast, efficient Python for numerical computing. With clear explanations, practical examples and real-world techniques, this guide takes you from array basics to advanced workflows while helping you understand how NumPy really works.