учебник по программированию

Machine Learning Algorithms in Depth, Smolyakov V., 2024.
     
   This book dives into the design of ML algorithms from scratch. Throughout the book, you will develop mathematical intuition for classic and modern ML algorithms and learn the fundamentals of Bayesian inference and deep learning as well as data structures and algorithmic paradigms in ML.
Understanding ML algorithms from scratch will help you choose the right algorithm for the task, explain the results, troubleshoot advanced problems, extend algorithms to new applications, and improve the performance of existing algorithms.

LLMs in Production, Brousseau C., Sharp M., 2025.
     
   LLMs in Production is not your typical Data Science book. In fact, you won’t find many books like this at all in the data space mainly because creating a successful data product often requires a large team—data scientists to build models, data engineers to build pipelines, MLOps engineers to build platforms, software engineers to build applications, product managers to go to endless meetings, and, of course, for each of these, managers to take the credit for it all despite their only contribution being to ask questions, oftentimes the same questions repeated, just trying to understand what’s going on.

Learn OpenCV with Python by Exercise, Stroup J.L.
     
   OpenCV is one of the most popular computer vision libraries. If you want to start your journey in the field of computer vision, then a thorough understanding of the concepts of OpenCV is of paramount importance.
In this article, I will try to introduce the most basic and important concepts of OpenCV in an intuitive manner.

Learn Go with Pocket-Sized Projects, Latour A., Chaiehloudj D., Bertrand P.
     
   This book is for developers who want to learn the language in a fun and interactive way, and be comfortable enough to use it professionally. Each chapter is an independent pocket-sized project. The book covers the specificities of the language, such as implicit interfaces and how they help in test design. Testing the code is included throughout the book. We want to help the reader become a good modern software developer while using the Go language.
This book also contains tutorials for command-line interfaces, and for both REST and gRPC microservices, showing how the language is great for cloud computing. It finishes with a project that uses TinyGo, the compiler for embedded systems.

LangChain in Action, MEAP, Infante R.
     
   "LangChain in Action" is structured to cater both to beginners and seasoned professionals. It offers a comprehensive look into the foundational technologies and advanced techniques in the realm of LLMs. Whether you are new to programming or an experienced developer, you will find the content approachable yet enriching, especially with practical code examples to enhance your engagement.
The insights in this book are drawn from my real-world experiences and continuous learning in a field that evolves almost daily. I cover a range of topics from running open source LLMs locally to advanced RAG techniques and fine-tuning methods. My goal is to provide a resource that I wish had been available to me—an accessible, practical guide that empowers you to navigate and excel in this emerging domain.

GPT-4, Руководство по использованию API Open AI, Аймен Эль Амри, 2024.
     
   В книге рассказывается о том, как использовать генеративные текстовые модели поколений GPT-3.5 и GPT-4 для создания приложений различного назначения, в числе которых интерактивный психотерапевт, интеллектуальный голосовой помощник, система рекомендации товаров, генератор заметок в соцсетях, система распознавания речи и многие другие. Вы научитесь использовать векторные базы данных, узнаете, как управлять уровнем креативности моделей GPT, применять современные методы генерирования высококачественного текста, и даже организуете диалог между двумя чат-ботами. Примеры и практические упражнения помогут закрепить пройденный материал.
Издание предназначено для тех, кто владеет основами языка программирования Python и собирается использовать GPT в реальных сценариях для решения прикладных задач.

Deep Learning Demystified, A Step-by-Step Introduction to Neural Networks, Shin K., 2024.
     
    This book is for anyone who wants to understand the basics of neural networks and deep learning. Whether you are a student, a professional, or a hobbyist, this book is designed to help you grasp the foundational concepts of neural networks. If you have little to no prior knowledge of artificial intelligence but are eager to learn, this book will provide you with the necessary tools and understanding to start your journey. Additionally, if you already have some experience with machine learning and want to deepen your knowledge of neural networks, this book will offer valuable insights and practical knowledge.

Building Applications with Large Language Models, Singh B., 2024.
     
    This book is your guide in understanding different ways in which Large Language Models, like GPT, BERT, Claude, LLaMA, etc., can be utilized for building something useful. It takes you on a journey starting from very basic, like understanding the basic models in NLP, to complex techniques, like PEFT, RAG, Prompt Engineering, etc. Throughout the book, you will find several examples and code snippets which will help you appreciate the state-of-the-art NLP models. Whether you’re a student trying to get hold of the new technology, a data scientist transitioning to the field of NLP, or simply someone who is inquisitive about Large Language Models (LLMs), this book will build your concepts and equip you with the knowledge required to start building your own applications using LLMs.