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In this guide, author Jean Frantz René presents essential industry-focused examples to establish the core principles of memory-safe C++. Every case study opens with code that compiles clean, survives a general-purpose lint, and gives you no reason to look twice. In each example you’ll find the defect yourself, take it apart at the machine level, reimplement the fix, and reflect on what you've learned. [Read more]

Learn the essentials of business optimization using reinforcement learning and AI models through relevant and useful business applications. You’ll apply RL to supply chains, marketing and ad campaigns, logistics, and even optimizing AI chatbots. Graphics, code samples, and math-lite explanations demonstrate the core theories of RL in an intuitive and illustrative way. [Read more]

AI agents are showing up everywhere—and most of them are being built by trial and error. This guide distills the experience of a growing community of agent builders into 20+ composable, reusable patterns for building AI agents that are reliable, efficient, controllable, and easy to reason about. In this uniquely-valuable book, author and NVIDIA Research scientist Peter Belcak gives you a durable engineering vocabulary and design intuition that outlasts any new model release. [Read more]

A practical, accessible introduction to generative AI for professionals, educators, managers, students, and content creators who want to understand how AI models, applications, and agents work so they can use it effectively. Based on author Matthew Berman’s wildly popular YouTube videos, this entertaining and insightful book guides you from square one without a lot of math, code, and technical jargon. [Read more]

Deliver the architecture, updating, adapting, and rebuilding your existing systems need so that autonomous AI agents can operate safely, responsibly, and at production scale. This book introduces authors Anjali Jain and Philip O'Shaughnessy’s Agentic Enterprise Framework: a domain-agnostic toolkit of patterns, playbooks, and reference models that you can apply to make almost any architecture ready for autonomous agents. [Read more]

AI Model Evaluation

Learn practical strategies for evaluating and assessing your machine learning models to streamline their scaling and deployment in production environments. This book covers key evaluation techniques—beginning with offline testing, then progressing through live A/B testing, shadow traffic rollouts, qualitative assessments, and LLM-driven feedback mechanisms, guiding you through measuring both model accuracy and overall system performance. [Read more]

Bayesian statistics is a framework for reasoning under uncertainty. It lets you incorporate prior knowledge, rigorously quantify uncertainty, and directly answer practical questions like: “what’s the probability that this new treatment improves outcomes by at least 10%?” Bayesian methods are more intuitive, flexible, and directly actionable, which makes them invaluable for data science, AI, experiment design, and beyond. This book teaches Bayesianism through clear explanations, rich illustrations, and relatable examples. [Read more]