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Посты канала
| 2 |  Grokking AI Algorithms: How AI solves complex problems, Second Edition (Final Release) 2026
Автор: Rishal Hurbans
Artificial Intelligence (AI) algorithms are the backbone of search and optimization, Deep Learning, Reinforcement Learning, and, of course, Generative AI. This book introduces the most important AI algorithms using relatable illustrations, interesting examples, and thought-provoking exercises. Written in simple language and with lots of visual references and hands-on code examples, it helps you build a natural intuition into how intelligent systems learn, plan, and adapt. This second edition has been thoroughly revised, with new chapters on large language models, image generation, and more. | 410 |
| 3 | Numeric Python Python Data Analysis with NumPy.pdf | 541 |
| 4 |  Numeric Python: Python Data Analysis with NumPy, Pandas, and Matplotlib 2026
Автор: Bernd Klein
This book teaches the Python fundamentals required to solve numerical problems in Data Science and Machine Learning.
Python is a general-purpose programming language used in a wide variety of fields – such as system administration, web development, computational linguistics, and, as already mentioned, numerical programming, where speed and memory usage are critical. Pure Python – that is, without optimized libraries – is not competitive with specialized tools like MATLAB or R for demanding numerical tasks. The performance of the algorithms used is of utmost importance in numerical applications. For this reason, Python relies on its powerful modules: NumPy, SciPy, Matplotlib, and Pandas. As a result, Python is now one of the leading languages in numerical programming – and is often more efficient than MATLAB or R. | 506 |
| 5 | Data Science First_True.pdf | 595 |
| 6 |  Data Science First: Using Language Models in AI-Enabled Applications 2026
Автор: John Hawkins
Proven, practical techniques for integrating language models into your Data Science workflows.
Data Science First: Using Language Models in AI-Enabled Applications, by Intersect AI’s Chief AI Officer John Hawkins, explains how practicing data scientists can integrate language models in Data Science workflows without abandoning essential principles of reliability, accuracy, and efficacy. Hawkins offers crystal-clear guidance on when, where, and how data scientists can integrate language models into their existing workflows without exposing themselves or their companies to unnecessary risks. | 552 |
| 7 | Generative AI Design Patterns_True.pdf | 824 |
| 8 |  Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications 2026
Автор: Valliappa Lakshmanan
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. | 720 |
| 9 | Azure AI Fundamentals (AI-900) Study Guide_True.pdf | 836 |
| 10 |  Azure AI Fundamentals (AI-900) Study Guide: In-Depth Exam Prep and Practice 2025
Автор: Tom Taulli
Businesses that want to stay competitive know that AI has become a crucial technology—and so do their employees looking to grow their careers. Earning Microsoft's AI-900: Azure AI Fundamentals certification proves your proficiency with foundational AI concepts. This study guide equips you with the knowledge needed to pass the AI-900 exam, whether you're an IT professional, a data analyst, or a student looking to break into the AI field.
Packed with clear explanations, real-world examples, exam tips, and practice questions, this comprehensive handbook is your go-to resource for mastering the Azure AI platform and advancing your career. You'll explore key exam topics, including machine learning, computer vision, and generative AI, while gaining practical insights into leveraging Azure's powerful AI tools. | 729 |
| 11 | Cilium_Up and Running_True.pdf | 747 |
| 12 |  Cilium: Up and Running: Cloud Native Networking, Security, and Observability 2026
Автор: Nico Vibert
Cilium is now considered the de facto cloud native networking platform for Kubernetes, connecting, securing, and monitoring millions of applications across thousands of clusters. With such versatility and feature-richness, Cilium can be daunting to learn. This comprehensive guide breaks Cilium down, making it broadly accessible to the increasing number of users who'll encounter the platform in their careers.
Nico Vibert, Filip Nikolic, and James Laverack, all from Isovalent (creators of eBPF and Cilium), take you through how Cilium works, the problems it can solve, and how to run it in production. If you're an experienced platform engineer or network architect who wants to get on top of the next big thing in cloud networking, this book is for you. | 677 |
| 13 | Large Language Models_ The Hard Parts_True.pdf | 789 |
| 14 |  Название: Large Language Models: The Hard Parts: Open Source AI Solutions for Common Pitfalls (Final Release) 2026
Автор: Thársis T.P.
Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Parts offers a clear, practical examination of the limitations developers and AI engineers face when building LLM-based applications. With a focus on implementation pitfalls (not just capabilities), this book provides actionable strategies supported by reproducible Python code and open source tools.
Readers will learn how to navigate key obstacles in application evaluation, input management, testing, and safety. Designed for builders and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs. | 674 |
| 15 | Introduction to Data Science for Engineering.pdf | 780 |
| 16 |  Introduction to Data Science for Engineering Students 2026
Автор: Ilias Bilionis
This book offers engineering students a concise and practical introduction to Data Science — no prior experience required. Designed specifically for those new to programming and statistical analysis, the book introduces the essential tools and concepts behind today's predictive AI systems.
Based on a proven course at Purdue University, Introduction to Data Science for Engineering Students equips students with core Data Science knowledge, such as Python programming, data analysis techniques, and key foundational statistical concepts necessary for predictive modelling. Through real-world engineering examples (e.g. predicting engine efficiency), students learn how to visualize and analyze real experimental data, apply probability to manage uncertainty, and learn how to build reliable predictive models step-by-step. | 697 |
| 17 | Hands-On AWS CDK_True.pdf | 840 |
| 18 |  Hands-On AWS CDK: Building Cloud Native Applications with Infrastructure as Code 2025
Автор: Sam Ward Biddle
Looking to accelerate development and build cloud native applications with AWS Cloud Development Kit? Through hands-on projects, you'll learn the basics of AWS CDK, the tool of choice for many of the world's largest technology companies.
Informed by real case studies and years of work with enterprise-scale cloud architectures, this book will benefit both novice and advanced cloud developers. It's complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions to help you build a shareable portfolio of completed projects, demonstrating your ability to build cloud infrastructure at scale. | 749 |
| 19 | Domain-Specific Small Language Models_Final.pdf | 852 |
| 20 |  Domain-Specific Small Language Models: Efficient AI for local deployment (Final Release) 2026
Автор: Guglielmo Iozzia
Domain-Specific Small Language Models teaches you how to create language models that deliver the power of LLMs for specific areas of knowledge. It provides a practical, application-focused counterpart to foundational texts like Sebastian Raschka’sBuild a Large Language Model (From Scratch), showing you how to adapt large-scale concepts for efficient, specialized use. You’ll learn to minimize the computational horsepower your models require, while keeping high–quality performance times and output. You’ll appreciate the clear explanations of complex technical concepts alongside working code samples you can run and replicate on your laptop. Plus, you’ll learn to develop and deliver RAG systems and AI agents that rely solely on SLMs, and without the costs of foundation model access. | 756 |
