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Deep Learning
An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a start-to-finish instruction book with complete coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep learningFulsome treatments of generative adversarial networks as well as deep Bayesian Neural networks.Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.
R 3,237.00
Computational Learning Theory
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
R 2,306.74
Advances and Applications in Deep Learning
Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike.
R 4,732.00
R 1,367.00
Derivative-Free Optimization
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book's structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.
R 6,996.69
Machine Learning and Intelligent Communication
This book constitutes the refereed post-conference proceedings of the 7th International Conference on Machine Learning and Intelligent Computing which was held in October 2022 in Jinhua, China. Due to COVID-19 pandemic the conference was held virtually. The 16 full papers of MLICOM 2022 were selected from 41 submissions and are clustered in thematical issues on applications of neural network and deep learning; intelligent massive MIMO communications; machine learning algorithms and intelligent networks.
R 2,729.18
Machine Learning and Soft Computing
This two part-volume CCIS constitutes the refereed proceedings of 9th International Conference, ICMLSC 2025, in Tokyo, Japan in January 24–26, 2025. The 39 full papers and 13 short papers included in this book were carefully reviewed and selected from 121 submissions. They follow the topical sections as below:Part I : Multimodal Data Analysis and Model Optimization; Basic Theories of Machine Learning and Emerging Application Technologies; and Intelligent Recommendation System Design and Privacy Security.Part II : Deep Learning Models and High-performance Computing; Data-driven Complex System Modeling and Intelligent Optimization Algorithms; and Image Analysis and Processing Methods based on AI.
R 3,895.87
Machine Learning In Pure Mathematics And Theoretical Physics
The terms 'machine learning', 'pure mathematics and theoretical physics' may first appear to be topics in two very different categories in mathematics and computer science, yet in the past five years, we have seen many articles published that combined machine learning techniques in the search for patterns using big data. The aim of this book, a first of its kind, is to collect research and survey articles from many experts in this emerging collaboration between theoretical mathematicians and computer scientists on topics ranging from combinatorics to number theory, to geometry, to quantum field theory and string theory.This book is put together with PhD students in mind as well as seasoned researchers, as a reference book for collection of publications that cross between mathematics and computer science.
R 6,295.12
Statistics and Machine Learning with R Workshop
Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inferenceKey FeaturesAdvance your ML career with the help of detailed explanations, intuitive illustrations, and code examplesGain practical insights into the real-world applications of statistics and machine learningExplore the technicalities of statistics and machine learning for effective data presentationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts.Starting with the fundamentals, you’ll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you’ll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You’ll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career.By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You’ll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.What you will learnHone your skills in different probability distributions and hypothesis testingExplore the fundamentals of linear algebra and calculusMaster crucial statistics and machine learning concepts in theory and practiceDiscover essential data processing and visualization techniquesEngage in interactive data analysis using RUse R to perform statistical modeling, including Bayesian and linear regressionWho this book is forThis book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.
R 1,693.00
Machine Learning
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications Ethics of machine learning including Bias, Fairness, Trust, Responsibility Basics of Deep learning, important deep learning models and applications Plenty of objective questions, Use Cases, Activity and Project based Learning ExercisesThe book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
R 9,401.37
