Deep Learning

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

Mastering Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

The latest version of Rust (1.85) has some great new features, like async closures, more stable associated function return types, and const generics that are now mature enough to underpin serious numerical libraries. The linfa and smartcore ecosystems have developed into decent classical machine learning stacks. The Burn training framework feels native to Rust, not like it's been ported from it. The Candle makes it so that loading pre-trained transformer models is more of an engineering task than a research exercise. The crates that used to need all sorts of workarounds now just work.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

هذا الكتاب هو دليل مختصر وموضّح بالرسوم لأي شخص يرغب في فهم الآلية الداخلية للنماذج اللغوية الضخمة، سواء في سياق المقابلات أو المشاريع أو بدافع الفضول الشخصي.

Este libro es una guía concisa e ilustrada para cualquiera que desee comprender el funcionamiento interno de los Grandes Modelos de Lenguaje, ya sea de cara a realizar entrevistas, proyectos o para satisfacer su curiosidad.

4. Who Should Read This Book?This book is specially designed for a wide audience:4.1 StudentsStudents of:Artificial intelligenceData scienceComputer scienceInformation technologyOperations researchApplied mathematicswill find this book essential for understanding foundations and applications of intelligent decision-making.4.2 ResearchersThis book helps researchers explore:Decision-making modelsPlanning algorithmsRisk-aware AIMathematical modelingOptimization under uncertaintyIt helps form a strong base for research projects and PhD work.4.3 Industry ProfessionalsEngineers and developers working on:RoboticsAutonomous vehiclesDecision support systemsPredictive analyticsAI toolsFinancial modelingwill find the algorithms, pseudocode, and frameworks highly practical.4.4 Faculty MembersTeachers and professors can use this book as:A primary textbookA reference guideA source of problems and case studiesA foundation for graduate and research courses 5. Learning OutcomesAfter studying this book, readers will be able to:Understand and construct utility functionsEvaluate rational choices under uncertaintyBuild decision treesConstruct influence diagramsDesign sequential decision systemsFormulate and solve MDPsApply POMDPs to real problemsImplement classical planning algorithmsModel multi-agent interactions using game theoryApply Bayesian decision theory to uncertain environmentsUnderstand the foundation of reinforcement learningBuild real-world decision and planning systemsThis ensures comprehensive mastery of both theory and practice.

Documentation isn’t just a chore—it’s a craft. And with AI tools at your side, it’s finally scalable, smart, and developer-friendly. Document Like a Developer: Technical Writing with AI Tools is your blueprint for building docs that ship with your code, evolve with your product, and speak your users’ language. Learn how to enforce style guides, automate glossary checks, run documentation sprints, and measure impact with precision. Whether you're a solo dev, a startup team, or scaling across an enterprise, this book gives you the workflows, templates, and mindset to treat documentation like a first-class citizen. Stop writing throwaway manuals. Start building living systems of clarity. The future of technical writing is here—and it’s automated, collaborative, and built for velocity.

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

This book is a quick foray into the world of deep learning-based computer vision and abnormal equipment sound detection. The readers are introduced to the ease with which powerful equipment and product quality monitoring solutions can be built using sound and visual data.

Learn supervised machine learning from the ground up through intuitive explanations, step-by-step mathematics, visual illustrations, and working Python code. Explore 66 chapters and six end-to-end case studies covering everything from linear regression to advanced neural networks.

Our starting point was raw CIFAR-10 bytes and a compiler check, and we ended up with residual networks, coroutine pipelines, compressed checkpoints and a real-time classifier built on two cooperating threads.Whenever we introduced a new technique, we gave it a contract, tested it early and measured it honestly. Our concepts rejected the wrong types before a build was finished, and static assertions caught shape mistakes long before training. Every claim in these chapters had an output we could reproduce.

From a single neuron to transformer-based LLMs in production — the deep-learning, NLP, and shipping capstone of the Data Science, End to End series, derived from first principles in Python.