Available now! Telegram Research 2025 — the year's key insights 
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Channel Posts
| 2 |  Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems (Final Release) 2026
Автор: Aurélien Géron
The potential of Machine Learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Perhaps you're ready to jump in, but you're unsure where or how to begin. Whether you're looking to enhance your skill set and apply Machine Learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place. | 261 |
| 3 | Learning Serverless Security_True.pdf | 458 |
| 4 |  Learning Serverless Security: Hacking and Securing Serverless Cloud Applications on Aws, Azure and Google Cloud 2026
Автор: Joshua Arvin Lat
Serverless computing now serves as a strategic backbone of modern cloud architectures, helping teams move faster and operate at scale. However, many still struggle to understand the security model of serverless computing. As more organizations migrate critical systems and sensitive data to the cloud using serverless architectures, this gap in serverless security knowledge increasingly exposes them to serious security incidents and data breaches.
This practical guide covers offensive and defensive security techniques to audit and secure serverless applications running on AWS, Azure, and Google Cloud. You'll explore how to attack and defend vulnerable serverless applications using step-by-step instructions. By the end of this book, you'll understand how to prevent various serverless application attacks and privilege escalation techniques. | 417 |
| 5 | Grokking_AI_Algorithms_2Ed_Final.pdf | 558 |
| 6 |  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. | 510 |
| 7 | Numeric Python Python Data Analysis with NumPy.pdf | 610 |
| 8 |  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. | 565 |
| 9 | Data Science First_True.pdf | 657 |
| 10 |  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. | 600 |
| 11 | Generative AI Design Patterns_True.pdf | 894 |
| 12 |  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. | 785 |
| 13 | Azure AI Fundamentals (AI-900) Study Guide_True.pdf | 836 |
| 14 |  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 |
| 15 | Cilium_Up and Running_True.pdf | 747 |
| 16 |  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 |
| 17 | Large Language Models_ The Hard Parts_True.pdf | 789 |
| 18 |  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 |
| 19 | Introduction to Data Science for Engineering.pdf | 780 |
| 20 |  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 |
