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Category: "Neural Networks"

Books

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

  2. The Hundred-Page Language Models Book
    hands-on with PyTorch
    Andriy Burkov

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

  3. Mastering Modern Time Series Forecasting
    A Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in Python (Preorder)
    Valery Manokhin

    Mastering Modern Time Series Forecasting is your all-in-one guide to building real-world forecasting systems that work — from classical stats to deep learning and beyond. Whether you're modeling retail demand or energy loads, this book gives you the tools, intuition, and code to go from zero to production. You'll cover ARIMA, ML, deep nets, transformers, and even the rise of FTSMs (Foundational Time Series Models). Written by a practitioner who’s built forecasting solutions for multibillion-dollar businesses, this is the hands-on, honest guide every data scientist, analyst, or forecaster needs.

  4. Deep Learning with PyTorch Step-by-Step
    A Beginner's Guide
    Daniel Voigt Godoy

    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.

  5. Turn data into decisions. Data Visualization Using Tableau for Data Scientists shows how interactive visuals, analytics, and storytelling come together to make complex data understandable, actionable, and impactful.

  6. Generative AI for Science
    A Hands-On Guide for Students and Researchers
    J. Paul Liu

    Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

  7. A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.

  8. ChatGPT for beginners
    A Quick Introduction to Prompt Engineering!
    Wasi

    This practical guide shows you how to harness ChatGPT and GPT-5 to boost productivity, create content, organize notes, and solve real-world problems efficiently.  Learn how to craft precise prompts, optimize responses, and apply AI ethically while avoiding common pitfalls like bias, repetitive outputs, and over-reliance. The book also covers the evolution of GPT models, the Transformer architecture, and essential techniques to maximize AI performance.  With hands-on examples, workflow integration tips, and insights into advanced AI tools, this guide empowers students, professionals, and hobbyists to confidently use AI in daily life.  Take control of AI, improve your efficiency, and explore new creative possibilities with this beginner-friendly, practical, and responsible guide to ChatGPT and GPT-5.  

  9. The inner workings of Large Language Models
    how neural networks learn language
    Roger Gullhaug

    I wanted to understand how ChatGPT and other large language models (LLMs) really work, so I read a lot of books, watched YouTube videos, asked hundreds of questions, and wrote it all down. This book is the result. If you want to understand how large language models like ChatGPT actually work, from tokens and vectors to transformers and training, this book will explain it in a clear, approachable way.

  10. Bridge the gap from C# basics to professional, high-performance AI systems. Master advanced polymorphism, Generics, and functional programming patterns. Learn to architect resilient AI pipelines and manage unmanaged GPU memory. Design and build an enterprise-grade, plugin-based chatbot application from scratch.

  11. Ditch slow, expensive AI APIs and bring the power of Large Language Models directly to your users' browsers. This guide teaches JavaScript and TypeScript developers how to build private, offline-capable, and blazing-fast AI applications. Master the local-first AI stack with Transformers.js, WebGPU, and Ollama to slash costs and own your data. Become a leader in the new era of serverless AI and deliver an instantaneous user experience

  12. Unlock the Black Box of Artificial Intelligence. 🧠✨ Stop simply calling API functions and start building the future. Volume 11 is a code-first masterclass that takes you from the mathematical roots of a single neuron to the cutting edge of Generative AI. Learn to build neural networks from scratch in NumPy before mastering PyTorch and Transformers. Don't just use AI—understand the "why" behind it.

  13. Mastering Advanced Time Series Forecasting in Python: Probabilistic, Hierarchical, and Foundation Models
    Master advanced forecasting with Python using machine learning, deep learning, and cutting-edge foundational models. Learn hierarchical and probabilistic forecasting, forecastability, metrics, and scalable pipelines. Build robust, real-world forecasting systems with production-ready code and expert guidance.
    Valery Manokhin

    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.

  14. Mastering Forecasting Metrics & Accuracy: For Data Science and BeyondForecasting models are only as good as the metrics used to measure them. Yet many teams still rely on outdated or misleading measures like MAPE. This book is the first comprehensive, practitioner-friendly guide dedicated entirely to forecast evaluation metrics — blending clear theory, Python recipes, and real-world case studies.Learn how to avoid common pitfalls, measure bias, handle intermittent demand, and apply advanced metrics like MASE, RMSSE, CRPS, pinball loss, and calibration scores. Each chapter includes formulas, code, and visuals to make concepts easy to apply.Perfect for data scientists, ML engineers, analysts, researchers, and industry professionals in retail, finance, and energy. No heavy math required.Living book: buy once, get free lifetime updates.Measure what matters.

  15. Probabilistic Forecasting with Conformal Prediction in Python
    The Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and Forecasting
    Valery Manokhin

    Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.