Books

I wrote this book to show exactly what happens when you add Rust to the kernel. Not as a toy. Not as a single driver in a conference demo. It's a full module that covers process management, memory allocation, file systems, networking, device drivers, inter-process communication, and even machine learning inference. It's all built on safe abstractions, all compiled into one loadable binary, all tested on a running kernel. You'll find that each chapter adds new source files to the same module.

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.

Transform the classic pizza.owl tutorial into a launchpad for cutting-edge semantic engineering! This hands-on guide bridges the gap between theory and practice, showing you exactly how to build ontology-based meta-models and deploy executable knowledge graphs. Master the "Diagramming as Code" methodologies of the Executable Knowledge Architecture (EKA) framework through a continuous, live-updated learning journey.

A book designed to help you transition from merely knowing Python to cracking Leetcode patterns. This book explains core DSA concepts through clear, intuitive analogies and walks you through the most important Leetcode patterns. If you're aiming to ace your next coding interview, this is the book I wish I had when I started.

Principios fundamentales para entender cómo organizar y manipular datos de manera eficiente. Con un enfoque didáctico y accesible.

Hyperscale Data Center Management reveals the architecture, operations, and strategy behind the world’s largest computing facilities. Discover how power, cooling, and network fabrics are engineered to serve billions; how automation and reliability principles keep services running through constant hardware failure; and how sustainability and economics shape every decision. From foundational definitions to the new demands of AI and GPU workloads, this is the complete guide to managing infrastructure that never sleeps. Essential reading for architects, operators, and leaders building for the future.

By diving into this book, students will:1.     Master Supervised Learning Fundamentals Gain strong conceptual understanding and practical competence.2.     Evate & Deploy Models Effectively Understand how to build, validate, interpret, and deploy high-quality models.3.     Think Critically about AI Systems Develop ethical awareness and critical reasoning regarding data bias and model behavior.

·        Comprehensive Learning Path: The book starts with basics and gradually leads you to advanced topics, making it accessible for beginners and challenging for advanced learners.·        Contextual AI Applications: Every concept is illustrated with AI and ML examples, ensuring relevance and immediate applicability.·        Enhanced Understanding of AI Models: Knowing data structures like trees and graphs clarifies how decision trees or knowledge graphs operate internally, boosting your model-building skills.·        Algorithm Efficiency Awareness: Understanding algorithm complexity and heuristics allows you to write optimized AI programs that can handle large datasets and real-time processing.·        Practical Coding Exercises: With implementations in Python, you will develop a coding mindset essential for AI practitioners.·        Preparation for Research and Development: The book equips you to contribute to AI research and innovate new algorithms or improve existing ones.

Most data modelling books teach you the craft. This one teaches you the reality. Seven chapters of honest, practical guidance from real project experience — covering conceptual modelling, governance, enterprise challenges, and the human side of data work that nobody else writes about.

Stop letting a lack of prep time hold your career back. Master 240 essential questions on algorithms, Java, and C in just a few hours—designed to turn daunting interview hurdles into your next big job offer.

The modern technical interview isn't about rote memorization—it's about demonstrating how you think. Master essential concepts like distributed systems, cloud-native architecture, and API design while learning the mental frameworks needed to showcase your engineering mindset. Stop searching for the "perfect answer" and start analyzing complex trade-offs like the top-tier engineer companies are looking for.

A practical interview reference covering Modern C++ language features, STL usage, concurrency, and performance (525 manuscript pages).

Master the future of technology with this definitive guide to Modern Data Science. Unlock actionable insights through Analytics, Machine Learning, and Big Data strategies. Perfect for beginners and pros wanting a logic-first approach to data-driven decision making.

A practical backend interview reference covering modern Java and Spring Boot development (1363 manuscript pages).

Stop being a syntax typist. Master Clean Code Architecture and Algorithmic Thinking to become a professional Software Architect in 2026. This blueprint teaches you to build Scalable Systems using Binary Logic, Big O Notation, and proven Software Engineering Patterns. Perfect for the modern Backend Developer, self-taught coder, and Tech Entrepreneur looking to design high-performance software from scratch.