Master Algorithms: From Foundations to AI bundle 
Build your algorithmic skills and knowledge from foundational principles to advanced data structures and algorithms to the AI algorithms that today’s intelligent systems demand. Master Algorithms: From Foundations to AI combines four books covering timeless algorithmic ideas, essential techniques every programmer should know, advanced data structures and problem-solving, and the algorithms behind modern AI. Whether you're strengthening your computer science fundamentals, striving to become a better programmer, or exploring how algorithms power intelligent systems, this collection offers a practical learning path for you.
The Learning Progression
Algorithmic building blocks >> Practical algorithms with common applications >> Advanced algorithmic problem-solving >> AI algorithms for modern systems
Timeless Algorithms, The Foundational Ideas 
Timeless Algorithms, The Foundational Ideas uses the insights of AI pioneers to help you diagnose failures, recognize hidden assumptions, and reason across the layers of your models and applications. Each chapter connects a common data tool to its seminal mathematics paper, revealing the “hidden stack”—a unique framework that maps the layers of modern intelligence from data to philosophy. With a focus on judgement and ethics, you’ll learn to design trustworthy systems, think probabilistically, and use automation wisely to build intelligent models that are not just effective, but principled.
Grokking AI Algorithms, Second Edition
Everything you’ll learn in this powerfully simple book is reinforced through engaging, end-to-end projects—from solving mazes with search algorithms to navigating a car through a crowded parking lot with reinforcement learning. Plus, this second edition has been thoroughly revised with fresh chapters exploring the core algorithms of LLMs and image generation models.
Algorithms Every Programmer Should Know
Classic algorithm textbooks are well known for encyclopedic depth, mathematical rigor, and extensive use of pseudocode to explain complex algorithms. Or, put plainly, they can be dense, boring, and hard to understand. Algorithms Every Programmer Should Know takes a more human approach. This highly approachable book drops you straight into a realistic reading group, letting you listen in as three interested individuals argue, question, and reason their way through some of the most important algorithms in computer science. Each chapter tackles one algorithm from the ground up. You’ll build your intuition through relatable analogies and back-and-forth discussion, then see the idea applied to real-world systems like routing, scheduling, string search, caching, and optimization. Once you’ve built your mental models, you’ll implement each algorithm in Python. Seeing your algorithms in action ensures you understand both how an algorithm works and why and when to reach for each one.
The algorithms you learn go far deeper than the usual interview list. Discover Gale–Shapley for stable matching, Rabin–Karp and Knuth-Morris-Pratt for string search, the Hungarian algorithm for assignment problems, Push-Relabel for network flow, Bron–Kerbosch for finding cliques, Adaptive Replacement Cache for smarter memory management, and more. Each chapter closes with reflective questions, real-world applications, and a concise summary you can return to before an interview or a design review.
You don’t need to read the book cover to cover, you can dip into any chapter as you need! Self-taught developers will get intuitive explanations that go deeper than rote memorization, students will find a friendly companion to their coursework, and working engineers will walk away with sharper mental models for choosing the right tool for the problem in front of them. Plus, Python notebooks and supplementary explainer videos make sure the ideas stick.
Fabulous Adventures in Data Structures and Algorithms
"Rigorous, curious, quietly funny, and extraordinarily generous with hard-won insight."—Scott Hanselman, Microsoft
Fabulous Adventures in Data Structures and Algorithms invites you to step off the beaten path and explore interesting, unfamiliar, and even exotic algorithms that will challenge your perspective and elevate your code. Legendary language designer Eric Lippert guides you with a refreshing, conversational approach, providing beautifully practical examples that highlight the recurring patterns behind stubborn coding problems.
You’ll begin with a fresh look at foundational, thread-safe, and persistent immutable data structures. Lippert demystifies stacks, queues, and finger-tree deques, illustrating how to manage memory efficiently through persistence. For developers working in highly concurrent, cloud native environments, these patterns will help you eliminate hard-to-spot race conditions and state-mutation bugs.
Your fabulous adventure then turns to structural search, compiler design, and the integration of functional programming within mainstream object-oriented languages. You’ll dive into Directed Acyclic Word Graphs (DAWGs), greedy pretty printers, and the complexities of tree unification. This section bridges theory and real-world tools, teaching you how to design highly modular compilers or robust static analysis engines and to write declarative, composable, and expressive APIs.
In the final leg of your journey, you’ll model randomness, statistical reasoning, and continuous probability—core concepts for AI and predictive analytics. Lippert introduces advanced Bayesian and monadic techniques, teaching you how to construct joint distributions and sample them using the powerful Metropolis algorithm. This statistical toolkit aligns perfectly with modern data-driven systems that need to make consistent, sound decisions under real-world uncertainty.
In the age of AI coding, implementation is becoming cheaper. AI can write code, but it cannot relieve developers of responsibility for understanding what the code means, why a given solution was chosen, how it will scale, and whether the problem has been framed correctly. This book develops exactly that judgment. It teaches the part that is becoming more valuable: recognizing, selecting, questioning, and evaluating the ideas behind implementations. It is an apprenticeship in computational taste, taught through unusual and memorable examples.
Some bundled books and liveVideos are part of the Manning Early Access Program. You'll get all the available content now, new content as it's created, and the final product when it's ready.
