Machine Learning

"How Does Computer Science Work?" answers 28 key questions every software engineer should know, unraveling the technologies that shape our digital world. From how browsers work and cryptography secures data to AI, Web3, and quantum computing, this book bridges curiosity and understanding. Perfect for students, self-taught programmers, and seasoned developers, it’s your ultimate guide to mastering foundational concepts and cutting-edge innovations in computer science.

Machine learning engineering is an in-demand skill set, and it can be difficult to find a helpful guide on the topic. This fully updated second edition will help you solve business problems by addressing the pain points in creating standardized pipelines for taking proof-of-concept ML models to production and producing trustworthy results.

Deep Learning for Time Series Cookbook covers several time series problems, and how to tackle them using deep learning in a set of coding recipes. These recipes will enable you to develop accurate forecasting models using the PyTorch ecosystem.

Bayesian inference uses probability distributions and Bayes' theorem to build flexible models. This book uses PyMC to abstract all mathematical and computational details from this process, allowing readers to solve a range of data science problems.

My goal is to equip other programmers with the confidence to confidently incorporate machine learning into their C++ projects by guiding them through real-world examples and addressing common challenges head-on.

This book offers a groundbreaking exploration of how advanced machine learning techniques are revolutionizing the diagnosis of Alzheimer's disease. In this comprehensive guide, you will uncover the powerful role that deep learning, particularly 3D Convolutional Neural Networks (3D-CNNs) and Attention Mechanisms, can play in early detection—paving the way for faster, more accurate diagnoses.

Dive deep into the world of Hugging Face and unlock the tools you need to create, fine-tune, and deploy state-of-the-art AI models. Part 3 of the Generative AI from Beginner to Paid Professional series is your complete guide to mastering Hugging Face’s powerful ecosystem through practical projects and real-world applications.

This edition includes a deeper exploration of machine learning and natural language processing, which I am excited to share. I have added new chapters on nonlinear models, multivariate techniques, and text analysis. You will find implementations of algorithms like Support Vector Machines, Neural Networks, and Principal Component Analysis, all using Rust's powerful crates such as smartcore, linfa, and tch. These examples prove that Rust is the ideal tool for complex data analysis tasks.

I've written this book with data scientists, machine learning engineers, and AI practitioners in mind. If you're looking for ways to make your workflows faster, more efficient, and less prone to errors, this book is a great resource to have on hand. Together, we'll figure out how to use JAX, fix any problems that come up, and see what's possible with advanced machine learning.

This edition marks a significant shift in PyTorch's approach to optimization, enhancing both performance and flexibility. The introduction of torch.compile() provides a tool that will significantly boost the training and inference speed of models. This update allows developers to maximize the potential of their neural networks without the need to rewrite them from scratch. 

"Unlock the full potential of machine learning with my comprehensive guide to supervised learning! From Multiple Linear Regression to advanced Ensemble techniques like Bagging, Boosting, Stacking, and Blending, this book covers everything you need to build powerful models with confidence. Perfect for beginners and experts alike, dive into clear explanations, practical examples, and essential evaluation metrics that will elevate your machine learning skills to the next level."

Transform your software creation process with AI Driven Development (AIDD). Learn to harness AI to generate programs, unlocking 10x - 20x productivity gains while building components, tests, and documentation. Written for software engineers, product managers, code conjurers and aspiring tinkerers, this book teaches you to express complex functional requirements using natural language with the precision of code, utilizing SudoLang - a language designed specifically for communicating ideas to AI language models. You'll learn to streamline your workflow, reduce code complexity, and craft more intelligent, responsive applications that adapt to user needs.Design and implement AI-powered applications using SudoLang, and any common programming language or framework. Examples will use JavaScript, Next.js, and React.