2nd Edition. — Springer, 2022. — 582 p. — ISBN 978-3-030-96622-5. This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book...
Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, Nisheeth Joshi. — John Wiley & Sons, Inc., 2022. — 271 p. — ISBN 978-1119775614. Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML)...
CRC Press, 2021. — 339 p. — ISBN: 978-0367336745. The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book...
O’Reilly, 2018. — 366 p. This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and...
New York: Springer, 2021. — 181 p. The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time...
Independently published, 2019. — 369 p. — ISBN: 978-1686500237, 1686500238. Your Guide to Getting Ahead with Python! Today, several commercial apps and research projects make use of machine learning, but this field is not only meant for big companies with extensive research teams, a beginner can get started, too. Machine Learning came into prominence in the 1990s, when...
Wiley, 2015. — 360 p. — ISBN: 978-1-118-96174-2. Целевая аудитория: опытные разработчики на Python. В настоящее время машинное обучение является одной из самых активных и быстро развивающихся областей науки. Результатом такой активности ML становится появление многочисленных библиотек и фреймворков языка Python для работы с данными, статистикой, математикой, биологией и многим...
Machine Learning Mastery, 2018. — 115 р. Welcome to XGBoost With Python. This book is your guide to fast gradient boosting in Python. You will discover the XGBoost Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own...
Leanpub, 2020. — 274 p. — ISBN: 978-1999579579. From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI...
2nd edition. — Morgan & Claypool, 2018. — 207 p. — ISBN: 978-1681733043. Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a...
2nd ed. — Packt Publishing, 2015. — 305 p. — ISBN: 9781784392772. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of...
2nd Edition. — Packt Publishing, 2019. — 371 p. — ISBN: 978-1-78899-417-0. Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you...
Ciml. info; Published by TODO, 2015. — 227 p. This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a...
New York: Springer, 2016. — 229 p. Guides the reader from the control of simple dynamical systems to real-world experiments assisted by ample supplementary material Contains interviews with leading experts in the field Offers extensive color figures with clear explanations Includes exercises at the end of every chapter and MatLAB codes for all examples This is the first...
Cambridge University Press, 2012. — 396 p. — ISBN: 978-1107096394. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to...
New York: Springer, 2019. — 492 p. Machine learning methods are now an important tool for scientists, researchers, engineers, and students in a wide range of areas. Many years ago, one could publish papers introducing (say) classifiers to one of the many fields that hadn’t heard of them. Now, you need to know what a classifier is to get started in most fields. This book is written...
Early Release. — 2nd edition. — O’Reilly, 2019. — 510 p. — ISBN: 978-1-492-03264-9. Concepts, Tools, and Techniques to Build Intelligent Systems Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs...
O’Reilly Media, 2018. — 45 p. — ISBN: 9781492033158. Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting...
McGraw-Hill Education, 2019. — 656 p. — ISBN: 978-1260456844, 1260456846. Cutting-edge machine learning principles, practices, and applications This comprehensive textbook explores the theoretical underpinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning...
Boca Raton: CRC Press, 2022. — 405 p. Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees...
CRC Press, 2019. — 364 p. — ISBN 13 978-1-138-58730-4. While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and...
New York: Springer, 2021. — 640 p. This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with...
Springer, 2023. — 102 p. This book is a state of the art resource that robotics researchers and engineers can use to make their robots and autonomous vehicles smarter. Readers will be able to describe metareasoning, select an appropriate metareasoning approach, and synthesize metareasoning policies. Metareasoning for Robots adopts a systems engineering perspective in which...
Packt Publishing, 2018. — 254 p. — ISBN: 978-1789806199. Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book DescriptionMachine...
O’Reilly Media, Inc., 2022. — 331 p. — ISBN 978-1-492-08525-6. • 2021-12-07: First Release 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...
Gistrup: River Publishers, 2023. — 251 p. This book provides various insights into Machine Learning techniques in healthcare system data and its analysis. Recent technological advancements in the healthcare system represent cutting-edge innovations and global research successes in performance modelling, analysis, and applications. The extensive use of machine learning in...
New York: Apress, 2018. — 355 p. Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine...
Bodenseo, 2021. — 453 p. Not only in Machine Learning but also in general life, especially business life, you will hear questiones like "How accurate is your product?" or "How precise is your machine?". When people get replies like "This is the most accurate product in its field!" or "This machine has the highest imaginable precision!", they feel fomforted by both answers....
Taylor & Francis Group, LLC, 2021. — 176 p. — ISBN: 978-0-367-27732-1. AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify...
BPB Publications, 2022. — 751 p. — ISBN 978-93-91030-42-1. Master the ML process, from pipeline development to model deployment in production. Key Features Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. A step-by-step approach to cover every data science task with utmost efficiency and highest performance. Access...
New York: Springer, 2021. — 341 p. This book presents applications of machine learning techniques in processing multimedia large-scale data. Multimedia such as text, image, audio, video, and graphics stands as one of the most demanding and exciting aspects of the information era. The book discusses new challenges faced by researchers in dealing with these large-scale data and...
New York: Springer, 2020. — 591 p. This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of Machine Learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on Machine...
John Wiley & Sons, Inc., 2019. — 307 p. — ISBN: 978-1-119-54563-7. Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on...
Springer, 2023. — 189 p. This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including: blind source separation; social network mining; image and video processing; array...
Springer, 2024. — 162 p. This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention...
The MIT Press, 2017. — 272 p. — ISBN: 0262036827. If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning - programming computers to learn from data - has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing,...
Publisher: Chapman and Hall/CRC; April, 2009, 457 pages. A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Linear discriminants The multi-layer perceptron Radial basis functions and splines Support vector machines Learning with trees Decision by committee: ensemble learning Probability and learning Unsupervised learning Dimensionality reduction...
2nd Edition. — Chapman and Hall/CRC, 2015. — 452 p. — ISBN13: 978-1-4665-8333-7. Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimization and Search....
Singapore: World Scientific, 2019. — 329 p. This is a comprehensive book on the theories of Artificial Intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these Machine Learning techniques and describes the problem of causality. This...
Singapore: World Scientific, 2021. — 321 p. This is a comprehensive book on the theories of Artificial Intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these Machine Learning techniques and describes the problem of causality. This...
Cambridge: Cambridge University Press, 2022. — 337 p. Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin...
Petaluma US : Roundtree Press, 2018. — 104 p. — ISBN: 978-1-944903-52-7. Artificial Intelligence Studio at Globant. Many industries are leveraging artificial intelligence (AI) to stay ahead of the curve. As cognitive and AI platforms become smarter, companies are using deep neural networks to give them abilities they didn’t have before. It’s the augmented intelligence...
CRC Press, 2022. — 212 p. — ISBN: 978-0-367-63453-7. The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature,...
2nd Edition. — The MIT Press, 2019. — 504 р. — (Adaptive Computation and Machine Learning series). — ISBN: 978-0262039406. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It...
Manning Publications, 2021. — 426 p. — ISBN 978-1617296741. Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction...
New York: Routledge, 2023. — 364 p. Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich,...
John Wiley & Sons, 2021. — 464 p. — (For Dummies). 2nd Edition Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android?as in the movie Ex Machina?it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving...
O’Reilly, 2017. — 392 p. — ISBN: 978-1-4493-6941-5. Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data...
MIT Press, 2012. — 1098 p. With the ever increasing amounts of data in electronic form, the need for automated methods for data analysis continues to grow. The goal of machine learning is to develop methods that can automatically detect patterns in data, and then to use the uncovered patterns to predict future data or other outcomes of interest. Machine learning is thus closely...
The MIT Press, 2023. — 1354 p. An advanced book for researchers and graduate students working in Machine Learning and statistics who want to learn about Deep Learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate...
John Wiley & Sons, 2012. — 404 p. — ISBN: 978-0470596692. Learn the science of collecting information to make effective decisions. Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed...
New York: Packt Publishing, 2016. — 983 p. Learn to solve challenging data science problems by building powerful machine learning models using Python. Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we...
San Francisco: No Starch Press, 2024. — 263 p. — ISBN 1718503768. Если вы готовы выйти за рамки вводных концепций и углубиться в машинное обучение, глубокое обучение и искусственный интеллект, формат вопросов и ответов сделает все быстро и просто для вас, без особых хлопот. Прямой, недвусмысленный подход этой книги, основанный на вопросах, которые часто задает автор Себастьян...
CRC Press, 2022. — 211 p. — ISBN 978-1-032-07401-6. Physics of Data Science and Machine Learning links fundamental concepts of physics to Data Science, Machine Learning and Artificial Intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data...
Springer, 2019. — 263 p. — ISBN: 978-3030157289, 3030157288. Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts...
CreateSpace Independent Publishing, 2018. — 106. — ISBN: 1719528403. Do You Want to Become An Expert Of Machine Learning? Start Getting this Book and Follow My Step by Step Explanations! This book is for anyone who would like to learn how to develop machine-learning systems. We will cover the most important concepts about machine learning algorithms, in both a theoretical and a...
Springer, 2019. — 320 p. — ISBN: 978-3-319-89802-5. The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many...
Manning Publications Co., 2021. — 513 p. — ISBN 978-1617295911. Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble...
Boca Raton: CRC Press, 2023. — 357 p. This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques...
Packt Publishing, 2016. — 439 p. — ISBN: 978-1-7858-8721-5. Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage...
Manning Publications, 2018. — 224 p. — ISBN: 978-1-6172-9333-7. Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web ap. Foreword by Sean Owen, Director of Data Science, Cloudera If you’re building machine learning models to...
Manning Publications Co., 2022. — 339 p. — ISBN: 978-1617298059. Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input. Optimize every stage of your machine learning pipelines with powerful automation...
Boca Raton: CRC Press, 2023. — 261 p. The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and...
New York: Springer, 2022. — 343 p. This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions...
Josh Starmer, 2022. — 305 p. Machine Learning is awesome and powerful, but it can also appear incredibly complicated. That’s where The StatQuest Illustrated Guide to Machine Learning comes in. This book takes the machine learning algorithms, no matter how complicated, and breaks them down into small, bite-sized pieces that are easy to understand. Each concept is clearly...
CRC Press, 2024. — 99 p. Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where...
New York: Springer, 2022. — 203 p. The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.
Springer, 2023. — 254 p. This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. The book starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from edge computing platforms. Using practical labs, each...
Birmingham: Packt Publishing, 2019. — 338 p. Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and...
Leanpub, 2019. — 133 p. Deep Learning has revolutionized the Machine Learning field. Python tools like Scikit-Learn, Pandas, TensorFlow, and Keras allows you to develop applications powered by Machine Learning.
BPB Publications, 2018. — 266 p. — ISBN: 9386551934. This book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the...
Boca RAton: CRC Press, 2020. — 329 p. The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR...
Springer, 2023. — 319 p. Machine Learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as Machine Learning has many...
World Scientific Publishing, 2023. — 418 p. — ISBN 9781800613690. The juxtaposition of "Machine Learning" and "pure mathematics and theoretical physics" may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated...
Springer, 2023. — 426 p. This book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of Machine Learning from both a theoretical and a practical perspective. The choice of the topics in the book is made based on one criterion: whether the practical utility of a certain method justifies its theoretical elaboration for...
Пер. с англ. С. Черников. — СПб.: Питер, 2023. — 256 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1773-4. Освойте ключевые навыки проектирования, разработки и развертывания приложений на базе машинного обучения (МО)! Пошаговое руководство по созданию МО-приложений с упором на практику: для специалистов по обработке данных, разработчиков программного обеспечения и...
Пер. с англ. — СПб.: БХВ-Петербург, 2024. — 448 с.: ил. — (От профессионалов для профессионалов). — ISBN: 978-5-9775-1903-8. Книга рассказывает о продвинутых приёмах машинного обучения и науки о данных (data science) на основе задач, решаемых на всемирно известной платформе Kaggle. Показано (в том числе на примере увлекательных интервью с Kaggle-гроссмейстерами), как устроена...
Пер. с англ. А. Логунов. — СПб.: БХВ-Петербург, 2022. — 640 с.: ил. — ISBN 978-5-9775-6763-3. Представлены фундаментальные знания и практические инструменты в области машинного обучения, в том числе более 100 углубленных упражнений на языке Python. Дано введение в машинное обучение и математическую оптимизацию, включая методы первого и второго порядков, градиентного спуска и...
СПб.: Питер, 2017. — 336 с. — (Библиотека программиста). — ISBN: 978-5-496-02989-6. Данная книга рассчитана на тех, кто хочет решать самые разнообразные задачи при помощи машинного обучения. Как правило, для этого нужен Python, поэтому в примерах кода используется этот язык, а также библиотеки pandas и scikit-learn. Вы познакомитесь с основными понятиями ML, такими как сбор...
СПб.: Питер, 2020. — 192 с. — (Библиотека программиста). — ISBN 978-5-4461-1560-0. Все, что вам действительно нужно знать о машинном обучении, может уместиться на паре сотен страниц. Начнем с простой истины: машины не учатся. Типичное «машинное обучение» заключается в поиске математической формулы, которая при применении к набору входных данных (называемых «обучающими данными»)...
Учебное пособие. — Ульяновск: УлГТУ, 2017. — 290 с. — ISBN 978-5-9795-1712-4. Учебное пособие рассматривает вопросы, связанные с анализом данных: модели, алгоритмы, методы и их реализацию на языке Python. Особое внимание уделено анализу временных рядов. Книга предназначена для студентов группы направлений 09, а также для студентов других групп направлений, изучающих дисциплины,...
М.: МЦНМО, 2013. — 390 с. Предназначено для первоначального знакомства с математическими основами современной теории машинного обучения (Machine Learning) и теории игр на предсказания. В первой части излагаются основы статистической теории машинного обучения, рассматриваются задачи классификации и регрессии с опорными векторами, теория обобщения и алгоритмы построения разделяющих...
СПб.: Питер, 2019. — 304 с. — (Для профессионалов). — ISBN: 978-5-4461-1061-2. Искусственный интеллект - это мощный инструмент в руках современного архитектора, разработчика и аналитика. Облачные технологии - ваш путь к укрощению искусственного интеллекта. Тщательно изучив эту незаменимую книгу от Ноя Гифта, легендарного эксперта по языку Python, вы легко научитесь писать...
Пер. с англ. С. Черников. — СПб.: Питер, 2024. — 512 с.: ил. — (Для профессионалов). — ISBN: 978-5-4461-1788-8. Фундаментальные математические дисциплины, необходимые для понимания машинного обучения, — это линейная алгебра, аналитическая геометрия, векторный анализ, оптимизация, теория вероятностей и статистика. Традиционно все эти темы размазаны по различным курсам, поэтому...
СПб.: Питер, 2019. — 432 с.: ил. — (IT для бизнеса). — ISBN 978-5-4461-1154-1. Маркос Лопез де Прадо делится тем, что обычно скрывают — самыми прибыльными алгоритмами машинного обучения, которые он использовал на протяжении двух десятилетий, чтобы управлять большими пулами средств самых требовательных инвесторов. Машинное обучение меняет практически каждый аспект нашей жизни,...
2-е издание. — СПб.: Диалектика, 2020. — 1040 с. — ISBN 978-5-907203-33-4. Благодаря серии выдающихся достижений глубокое обучение значительно усилило всю область машинного обучения. В наше время даже программисты, почти ничего не знающие об этой технологии, могут использовать простые и эффективные инструменты для реализации программ, которые способны обучаться на данных. Новое...
СПб.: Питер, 2019. — 512 с.: ил. — (IT для бизнеса). — ISBN 978-5-4461-0926-5. Наука о данных становится неотъемлемой частью любой маркетинговой деятельности, и эта книга является живым портретом цифровых преобразований в маркетинге. Анализ данных и интеллектуальные алгоритмы позволяют автоматизировать трудоемкие маркетинговые задачи. Процесс принятия решений становится не...
М.: ДМК Пресс, 2018. — 252 с. Н20 - простая в использовании и открытая библиотека, которая поддерживает большое количество операционных систем и языков программирования, а также масштабируется для обработки больших данных. Эта книга научит вас использовать алгоритмы машинного обучения, реализованные в Н2О, с упором на наиболее важные для продуктивной работы аспекты. Рассмотрены...
Пер. с англ. — СПб.: БХВ-Петербург, 2022. — 450 с.: ил. — ISBN 978-5-9775-6797-8. Приводимые в книге паттерны проектирования отражают лучшие практические подходы к решению типичных задач машинного обучения. Указанные паттерны, реализованные в программном коде, сконцентрировали опыт сотен экспертов в простые и легкодоступные советы. Книга содержит подробный разбор 30 паттернов,...
М.: Альпина ПРО, 2021. — 335 с.— ISBN 978-5-907394-29-2 Мы живем во время революции, еще 50 лет назад казавшейся невероятной, — революции в области умных машин, которые теперь обучаются самостоятельно, вместо того чтобы просто выполнять запрограммированные команды. И возможности таких машин огромны: распознавание изображений, лиц и голосов, переводы на сотни языков, беспилотное...
Издание автора, 2022. — 480 с.: 433 ил. — ISBN 978-5045110815, 5045110810. Эта книга будет интересна всем, кто хочет научиться применять Python 3 при разработке в области искусственного интеллекта и машинного обучения. С этой книгой Вы познакомитесь с основными терминами и понятиями ИИ, такими как машинное обучение, глубокое обучение и нейронные сети. Научитесь создавать...
М.: O’Reilly Media, 2017. — 392 с. Машинное обучение стало неотъемлемой частью различных коммерческих и исследовательских проектов, однако эта область не является прерогативой больших компаний с мощными аналитическими командами. Даже если вы еще новичок в использовании Python, эта книга познакомит вас с практическими способами построения систем машинного обучения. При всем...
СПб.: Питер, 2025. — 608 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-4191-3. Машинное обучение давно уже стало синонимом искусственного интеллекта. Оно проникло во многие аспекты нашей жизни и стало одной из важнейших областей современной науки. Эта книга — путеводитель по ключевым идеям машинного обучения. Вы узнаете, как методы машинного обучения получаются из...
Пер. с англ. и ред. Ю.Н. Артеменко. — O’Reilly Media, lnс; СПб.: Альфа-книга, 2019. — 688 с.: ил. — ISBN: 978-5-9500296-2-2 (рус.), ISBN: 978-1-491-96229-9 (англ.). "Эта книга - замечательное введение в теорию и практику решения задач с помощью нейронных сетей. Она охватывает ключевые моменты, необходимые для построения эффективных приложений, а также обеспечивает достаточную...
Пер. с англ. канд. хим. наук А.Г. Глузикевича, под ред. В.Р. Гинзбурга. — СПб..: Диалектика, 2020. — 432 с.: ил. — ISBN 978-5-907144-99-6. По мнению многих отраслевых экспертов, обучение без учителя — передовой рубеж технологий искусственного интеллекта (ИИ) и, возможно, ключ к созданию сильного ИИ. Поскольку подавляющая часть накопленных в мире данных не размечена, к ним...
Астана: Фолиант, 2024. — 688 с. — ISBN 978-601-11-0034-2. Предисловие : Дмитро Джулгаков, ведущий специалист PyTorch Core. Исчерпывающее руководство по машинному (МО) и глубокому обучению с использованием языка программирования Python, фреймворка PyTorch и библиотеки scikit-learn. Рассмотрены основы МО, алгоритмы для задач классификации, классификаторы на основе scikit-learn,...
Ташкент, 2015. — 27 с. Теория обобщающей способности - ТОСАМО является альтернативной для наиболее распространённой на сегодняшний день Статистической теории машинного обучения Владимира Вапника. ТОСАМО с помощью математической теории игр и линейной алгебры отвечает на вопросы, не имевшие ответов в предшествующих теориях: 1. Почему максимальная обучающая способность не не всегда...
М.: Диалектика, 2020. — 768 с. В книге излагаются основы статистического обучения для решения практических задач, возникающих в медицине, биологии, финансах и многих других отраслях науки и промышленности. В частности, рассматриваются основные понятия и методы статистического обучения: линейная регрессия, нелинейная регрессия, линейные методы классификации, регуляризация,...
Пер. с англ. А. А. Слинкина. — М.: ДМК Пресс, 2015. — 400 c.: ил. — ISBN: 978-5-97060-273-7, 600 dpi, OCR. Перед вами один из самых интересных учебников по машинному обучению - разделу искусственного интеллекта, изучающего методы построения моделей, способных обучаться, и алгоритмов для их построения и обучения. Автор воздал должное невероятному богатству предмета и не упустил...
М.: ДМК Пресс, 2015. — 400 c. — ISBN 9785970602737, 5970602736. Перед вами один из самых интересных учебников по машинному обучению – разделу искусственного интеллекта, изучающего методы построения моделей, способных обучаться, и алгоритмов для их построения и обучения. Автор воздал должное невероятному богатству предмета и не упустил из виду объединяющих принципов. Читатель с...
Пер. с англ. — СПб: БХВ-Петербург, 2019. — 384 с.: ил. — ISBN: 978-5-9775-4056-8. Книга содержит около 200 рецептов решения практических задач машинного обучения, таких как загрузка и обработка текстовых или числовых данных, отбор модели, уменьшение размерности и многие другие. Рассмотрена работа с языком Python и его библиотеками, в том числе pandas и scikit-leam. Решения всех...
Пер. с англ. Логунов А. — СПб.: БХВ-Петербург, 2020. — 560 с. — ISBN 978-5-9775-6595-0. Книга посвящена практике применения машинного обучения с целью создания мощных алгоритмических стратегий для успешной торговли на финансовых рынках. Изложены базовые принципы работы с данными: оценивание наборов данных, доступ к данным через API на языке Python, доступ к финансовым данным на...
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