Книги по Machine Learning (ML)

This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Pe...

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques.Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular...

Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process.In this hands-on guide, author Anthony Sarkis--lead engineer for the Diffgram AI traini...

Узнайте тонкости работы алгоритмов ML, чтобы эффективно решать задачи и повышать производительность используемых моделей. Познакомьтесь с фундаментальными математическими основами важнейших алгоритмов машинного обучения и вариантами их реализации на Python. Особое внимание уделяется вероятностным методам.В книге анализируются и объясняются десятки алгоритмов, применяемых в различных сферах, в част...

In the simplest terms, event-driven architectures are like onions; they are manageable as a single layer (like a monolith) but when you get into them, they begin to cascade apart and you quickly realize that there are many complex layers (distributed microservices architecture). And that’s when the tears begin.This prescriptive guide takes you through the steps of moving a platform with millions o...

Why We Wrote This BookThroughout our experience, we’ve worked with various APIs, including REST, Query, RPC, broker-based systems, and more. Every API style has an ecosystem surrounding it: community, working groups, and sometimes internet giants supporting it. For every style, you may read many books to discover API merits; and believe us, it takes time to master just one style. Yet we can no lon...

Learn how to write and run Ansible Playbooks, from the basics to launching complex multi-tier applications across public cloud platforms such as Amazon Web Services (AWS) and Microsoft AzureKey FeaturesWrite roles to automate everything, from basic apps to the entire cloud infrastructureLeverage Ansible's module ecosystem to streamline tasks across cloud platforms, operating systems, and appsAdopt...

This new volume provides in-depth and detailed knowledge about the latest research in image processing and computer vision techniques. Explaining the machine learning algorithms and models involved, the authors differentiate between the various algorithms available and how to choose which to use for the most precise results for a specific task involving certain constraints. The volume provides rea...

Deep learning made simple.Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel.After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a th...

Master Zig's blend of safety and performance by building real-world systems applications, and learn memory management through to compile-time execution to create reliable software with full control and zero hidden behaviorsKey FeaturesWrite high-performance code with Zig's clear, no-surprises approach to control flowDiscover memory management techniques to get complete control without sacrificing...

Artificial intelligence is redefining how software is created, enabling developers to code faster, improve accuracy, and bring innovative ideas to life. In today’s competitive technology landscape, AI-assisted programming is no longer optional; it’s a core skill for building modern web applications and machine learning solutions.This book systematically guides you through the whole development cyc...

Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.In Deep Learning with Python, Third Edition you’ll discover:De...

Узнайте, что происходит внутри черного ящика! Для использования глубокого обучения вам придется подготовить данные, выбрать правильную модель, обучить ее, оценить качество и точность и предусмотреть обработку неопределенности и изменчивости в выходных данных развернутого решения. Эта книга шаг за шагом знакомит с основными математическими концепциями, которые пригодятся вам как специалисту по данн...

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to r...

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments...