Machine learning

A mathematical introduction to deep learning, written by a group of leading experts in the field.

Now in paperback: the new classic on the theory of statistical inference in statistical models with an infinite-dimensional parameter space.

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

A diverse selection of data science topics explored through a mathematical lens.

The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data.

This book gathers selected research papers presented at the Fifth International Conference on Communication and Intelligent Systems (ICCIS 2023), organized by Malaviya National Institute of Technology Jaipur, India, during December 16-17, 2023.

Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL).

This book presents the select proceedings of 3rd International Conference on Sports Engineering (ICSE 2023).

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD).

This book discusses technology and systems to create valuable ideas from data through the construction of knowledge.

This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing.

Businesses that want to stay competitive know that AI has become a crucial technologyand so do their employees looking to grow their careers.

This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face.

This book brings a thorough explanation on the path needed to use cloud computing technologies to run High-Performance Computing (HPC) applications.

Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions.

This book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R.

This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard.

How we think and react has a direct impact on experience design, but often designers don't understand the "e;whys"e; behind their best practices, leaving them at risk for misusing or underutilizing those designs.

This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software.

This book is a collection of selected research papers presented at the 2023 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), held in Berlin, Germany, on June 30 - July 2, 2023.

This book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations.

This book describes theoretical advances in the study of artificial neural networks.

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence.

This book offers insight into the current issues of the merger between reliability engineering and computational intelligence.

The first book to present the basic techniques and main results of this expanding field of research.

This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes.

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory.

A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Covers text mining and link detection algorithms, pre-processing techniques, knowledge representation, visualization and applications.

A coherent source for applications including robotics, computational biology, graphics, manufacturing, aerospace applications and medicine.

Artificial neural networks, learning, statistical mechanics; background material in mathematics and physics; examples and exercises; textbook/reference.

* This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science * Most significant update to the second edition is the MATLAB codes that accompany the text * Provides a thorough discussion of hybrid genetic algorithms * Features more examples than first edition

An interdisciplinary framework for learning methodologies covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data.

This book will show how generative technology works and the drivers.

As technology continues to advance, the Internet is evolving into what is often referred to as Web 3.

This book presents the select proceedings of 3rd International Conference on Sports Engineering (ICSE 2023).

The book is a collection of the best-selected research papers presented at the International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications (ICMISC 2023) held in September 2023 at the CMR Institute of Technology, Hyderabad, Telangana, India.

This book provides a comprehensive review and in-depth study on efficient beamforming design and rigorous performance analysis in mmWave networks, covering beam alignment, beamforming training and beamforming-aided caching.

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlowKey FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook DescriptionWith the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects.

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook DescriptionDeep learning is the driving force behind many recent advances in various computer vision (CV) applications.

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithmsKey FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook DescriptionWith significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examplesKey FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook DescriptionAI has the potential to replicate humans in every field.

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learningKey FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook DescriptionWith deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions.

Get well-versed with traditional as well as modern natural language processing concepts and techniquesKey FeaturesPerform various NLP tasks to build linguistic applications using Python librariesUnderstand, analyze, and generate text to provide accurate resultsInterpret human language using various NLP concepts, methodologies, and toolsBook DescriptionNatural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text.