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Computer Science

  1. Mastering STM32 - Second Edition
    Mastering STM32 - Second Edition
    A step-by-step guide to the most complete ARM Cortex-M platform, using the official STM32Cube development environment
    Carmine Noviello

    With more than 1200 microcontrollers, STM32 is probably the most complete ARM Cortex-M platform on the market. This book aims to be the most complete guide around introducing the reader to this exciting MCU portfolio from ST Microelectronics and its official CubeHAL and STM32CubeIDE development environment.

  2. The Hundred-Page Language Models Book
    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. The Hundred-Page Machine Learning Book

    Everything you really need to know in Machine Learning in a hundred pages.

  4. The Agentic AI book
    The Agentic AI book
    From Language Models to Multi-Agent Systems
    Dr. Ryan Rad

    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.

  5. Certainty by Construction
    Certainty by Construction
    Software and Mathematics in Agda
    Sandy Maguire
    No Description Available
  6. Code a database in 45 steps (Go)
    Code a database in 45 steps (Go)
    a series of test-driven small coding puzzles
    Lowram Eepson

    This series of test-driven small coding puzzles lets you code a database from scratch (no dependencies).We'll cover KV storage engines, LSM-Tree indexes, SQL, concurrent transactions, ACID, etc.

  7. Mastering CatBoost: The Hidden Gem of Tabular AI
    Mastering CatBoost: The Hidden Gem of Tabular AI
    Harness the Power of CatBoost for Tabular Data and Beyond
    Valery Manokhin

    Unlock the full potential of CatBoost — a powerful gradient boosting library built for structured/tabular data and still underused in practice.In Mastering CatBoost: The Hidden Gem of Tabular AI, you’ll learn how to take advantage of CatBoost’s key strengths: native categorical feature handlingstrong accuracy, and fast inference — without brittle preprocessing pipelines.Written for data scientists, ML engineers, and applied researchers, the book covers:Real-world use cases and end-to-end workflowsPractical tuning strategies and diagnosticsInterpretability with SHAP, feature importance, and constraintsDeployment-minded best practices and common failure modesWhether you’re new to CatBoost or ready to go deeper, this is a clear, practical guide to building high-performance tabular models.Early Access is available now, with ongoing updates leading to the full release in 2026.

  8. Discrete Mathematics for Computer Science
    Discrete Mathematics for Computer Science
    Alexander S. Kulikov, Alexander Golovnev, Alexander Shen, Vladimir Podolskii, and Marie Brodsky

    This book supplements the DM for CS Specialization at Coursera and contains many interactive puzzles, autograded quizzes, and code snippets. They are intended to help you to discover important ideas in discrete mathematics on your own. By purchasing the book, you will get all updates of the book free of charge when they are released.

  9. Build Your Own Database in Go From Scratch
    Build Your Own Database in Go From Scratch
    From B+tree to SQL in 3000 lines
    build-your-own.org

    Learn databases from the bottom up by coding your own, in small steps, and with simple Go code (language agnostic).Atomicity & durability. A DB is more than files!Persist data with fsync.Crash recovery.KV store based on B-tree.Disk-based data structures.Space management with a free list.Relational DB on top of KV.Learn how tables and indexes are related to B-trees.SQL-like query language; parser & interpreter.Concurrent transactions with copy-on-write data structures.

  10. Super Study Guide: Algorithms & Data Structures
    Super Study Guide: Algorithms & Data Structures
    Afshine Amidi and Shervine Amidi

    A concise, illustrated guide to algorithms and data structures, perfect for coding interviews, classes, or self-study. Covers key concepts, from fundamentals to graphs, trees, sorting, and search techniques.

  11. My Adventures with Large Language Models
    My Adventures with Large Language Models
    Build foundational LLMs from Transformers to DeepSeek, from scratch, in PyTorch.
    Prathamesh S.

    Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.

  12. Foundations of Computing
    Foundations of Computing
    An Accessible Introduction to Formal Languages
    Charles D. Allison

    An accessible, practical approach to formal languages with an introduction to computability.

  13. エージェンティックAI ブック
    エージェンティックAI ブック
    言語モデルからマルチエージェントシステムへ
    Dr. Ryan Rad

    AIエージェントの構築が、これほど容易だった時代はない。そして、実際に機能するものを作ることが、これほど難しい時代もない。本書は言語モデルの基礎から本番対応マルチエージェントシステムまで、失敗が起こる前に予測し、壊滅的な障害ではなく優雅な劣化を設計し、完全なアーキテクチャの所有権を確立するための深さをもって、あなたを導く。ペーパーバック版はamazonにて好評発売中。

  14. Requirements-Skills erfolgreicher Softwareteams
    Requirements-Skills erfolgreicher Softwareteams
    Praxisbuch zum iSAQB CPSA-Advanced Req4Arc
    Gernot Starke and Peter Hruschka

    Fühlen Sie sich als Entwicklungsteam von Requirements-Engineers, Product-Owner oder Produktmanager bezüglich klarer Anforderungen im Stich gelassen? Hier finden Sie methodische und pragmatische Ansätze, mit denen Sie dieser Misere entkommen können.

  15. Document Like a Developer: Technical Writing with AI Tools
    Document Like a Developer: Technical Writing with AI Tools
    Write Once, Scale Everywhere: The New Era of Technical Communication
    Kevin Languedoc

    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.