---

Ensemble methods that train multiple learners and then combine them to use, with Boosting and Bagging as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks.

Twelve years have passed since the publication of the first edition of the book in 2012 (Japanese and Chinese versions published in 2017 and 2020, respectively). Many significant advances in this field have been developed. First, many theoretical issues have been tackled, for example, the fundamental question of why AdaBoost seems resistant to overfitting gets addressed, so that now we understand much more about the essence of ensemble methods. Second, ensemble methods have been well developed in more machine learning fields, e.g., isolation forest in anomaly detection, so that now we have powerful ensemble methods for tasks beyond conventional supervised learning.

Third, ensemble mechanisms have also been found helpful in emerging areas such as deep learning and online learning. This edition expands on the previous one with additional content to reflect the significant advances in the field, and is written in a concise but comprehensive style to be approachable to readers new to the subject.

[Lire la suite]

---

---

A Concise Introduction to Functional Analysis is designed to serve a one-semester introductory graduate (or advanced undergraduate) course in functional analysis.

The text is pragmatically structured so that each unit corresponds to one class, with the hope of being helpful for both students and teachers. It is expected that this text will provide students with a strong general understanding of the subject, and that they should feel well equipped to take on the more advanced texts and courses covering topics not treated here.

Features

Numerous examples and counterexamples to illustrate such abstract concepts
Over 430 exercises, with partial solutions included in the book itself
Minimal pre-requisites beyond linear algebra and general topology.


[Lire la suite]

---

---

Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs.

This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs.

Design around the limitations of LLMs
Ensure that generated content follows a specific style, tone, or format
Maximize creativity while balancing different types of risk
Build agents that plan, self-correct, take action, and collaborate with other agents
Compose patterns into agentic applications for a variety of use cases


[Lire la suite]

---

---

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.

This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently.

Understand the distinct features of foundation model-enabled AI agents
Discover the core components and design principles of AI agents
Explore design trade-offs and implement effective multiagent systems
Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field


[Lire la suite]

---

---

Foundation models have enabled many new AI use cases while lowering the barriers to entry for building AI products. This has transformed AI from an esoteric discipline into a powerful development tool that anyone can use—including thos with no prior AI experience.

In this accessible guide, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of application patterns. The book also introduced a practical framework for developing an AI application and efficiently deploying it.

Understand what AI engineering is and how it differs from traditional machine learning engineering
Learn the process for developing an AI application, the challenges at each step, and approaches to address them
Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work
Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them
Choose the right model, dataset, evaluation benchmarks, and metrics for your needs


[Lire la suite]

---

---

Here's the thing about large language models: they don't play by the old rules. Traditional MLOps completely falls apart when you're dealing with GenAI. The model hallucinates, security assumptions crumble, monitoring breaks, and agents can't operate. Suddenly you're in uncharted territory. That's exactly why LLMOps has emerged as its own discipline.

LLMOps: Managing Large Language Models in Production is your guide to actually running these systems when real users and real money are on the line. This book isn't about building cool demos. It's about keeping LLM systems running smoothly in the real world.

Navigate the new roles and processes that LLM operations require
Monitor LLM performance when traditional metrics don't tell the whole story
Set up evaluations, governance, and security audits that actually matter for GenAI
Wrangle the operational mess of agents, RAG systems, and evolving prompts
Scale infrastructure without burning through your compute budget


[Lire la suite]

---