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Model-Based Machine Learning
Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.Key Features:· Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems.· Explains machine learning concepts as they arise in real-world case studies.· Shows how to diagnose, understand and address problems with machine learning systems.· Full source code available, allowing models and results to be reproduced and explored.· Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.
R 4,066.00
Advanced Machine Learning with R
Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languagesKey FeaturesGain expertise in machine learning, deep learning and other techniquesBuild intelligent end-to-end projects for finance, social media, and a variety of domainsImplement multi-class classification, regression, and clusteringBook DescriptionR is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. You’ll also be introduced to reinforcement learning along with its various use cases and models. Additionally, it shows you how some of these black-box models can be diagnosed and understood.By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.This Learning Path includes content from the following Packt products:R Machine Learning Projects by Dr. Sunil Kumar ChinnamgariMastering Machine Learning with R - Third Edition by Cory LesmeisterWhat you will learnDevelop a joke recommendation engine to recommend jokes that match users’ tastesBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networksMake predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationProduce simple and effective data visualizations for improved insightsUse NLP to extract insights for textImplement tree-based classifiers including random forest and boosted treeWho this book is forIf you are a data analyst, data scientist, or machine learning developer this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path.
R 2,268.88
Applied Intelligent Decision Making in Machine Learning
The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.
R 3,980.46
R 7,326.00
Implementations and Applications of Machine Learning
This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning.
R 5,610.36
R 611.00
Practical Machine Learning with H20
Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You'll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work
R 1,140.00
Machine Learning, Optimization, and Data Science
This two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021.The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
R 3,950.81
Machine Learning for Imbalanced Data
Raise your machine learning game and deal with imbalanced data using libraries, such as imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy, and squeeze better performance from machine learning models using this essential guideKey FeaturesThe book is packed with detailed explanations, illustrations, and code samples using modern machine learning frameworksLearn cutting edge deep learning techniques to overcome data imbalanceThe book has a comprehensive coverage of methods for dealing with skewed data in ML and DL applicationsBook DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to a suboptimal performance on imbalanced data. Addressing class imbalance is crucial for significantly improving model performance.Machine Learning for Imbalanced Data begins by introducing the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance performance on imbalanced data when using classical machine learning models, including various sampling and cost-sensitive learning methods.As you progress, the book delves into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples provide working, reproducible code that demonstrates the practical implementation of each technique.By the end of this book, you will be adept at identifying and addressing class imbalances, and confidently applying various techniques including sampling, cost-sensitive techniques, and threshold adjustment when using traditional machine learning or deep learning models.What you will learnEffectively use imbalanced data in your ML modelsExplore the metrics used when classes are imbalancedUnderstand how and when to apply various sampling methods such as over-sampling and under-samplingApply data-based, algorithm-based, and hybrid approaches for dealing with class imbalanceCombine and choose from various options for data balancing while avoiding the common pitfallsUnderstand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasetsWho this book is forThis book is for machine learning practitioners, who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core ML concepts will help readers maximize the benefits and insights gained from this comprehensive resource.
R 1,693.00
Ensemble Machine Learning
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
R 6,815.05
Thoughtful Machine Learning
Learn how to apply test-driven development (TDD) to machine-learning algorithms - and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
R 1,488.00
R 6,961.00
Mathematical Theories of Machine Learning - Theory and Applications
This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing step sizes of gradient descent for escaping strict saddle points in non-convex optimization problems. In the second part, the authors propose algorithms to find local minima in nonconvex optimization and to obtain global minima in some degree from the Newton Second Law without friction. In the third part, the authors study the problem of subspace clustering with noisy and missing data, which is a problem well-motivated by practical applications data subject to stochastic Gaussian noise and/or incomplete data with uniformly missing entries. In the last part, the authors introduce an novel VAR model with Elastic-Net regularization and its equivalent Bayesian model allowing for both a stable sparsity and a group selection.
R 4,375.54
Machine Learning for Mobile
Leverage the power of machine learning on mobiles and build intelligent mobile applications with easeKey FeaturesBuild smart mobile applications for Android and iOS devicesUse popular machine learning toolkits such as Core ML and TensorFlow LiteExplore cloud services for machine learning that can be used in mobile appsBook DescriptionMachine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples.You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains.By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.What you will learnBuild intelligent machine learning models that run on Android and iOSUse machine learning toolkits such as Core ML, TensorFlow Lite, and moreLearn how to use Google Mobile Vision in your mobile appsBuild a spam message detection system using Linear SVMUsing Core ML to implement a regression model for iOS devicesBuild image classification systems using TensorFlow Lite and Core MLWho this book is forIf you are a mobile app developer or a machine learning enthusiast keen to use machine learning to build smart mobile applications, this book is for you. Some experience with mobile application development is all you need to get started with this book. Prior experience with machine learning will be an added bonus
R 1,498.00
R 531.37
Graph Machine Learning
Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric, and DGLKey FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.What you will learnImplement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGLApply graph analysis to dynamic datasets using temporal graph MLEnhance NLP and text analytics with graph-based techniquesSolve complex real-world problems with graph machine learningBuild and scale graph-powered ML applications effectivelyDeploy and scale your application seamlesslyWho this book is forThis book is for data scientists, ML professionals, and graph specialists looking to deepen their knowledge of graph data analysis or expand their machine learning toolkit. Prior knowledge of Python and basic machine learning principles is recommended.
R 1,791.00
Machine Learning for Engineers
All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.
R 2,917.20
Mean Field Guided Machine Learning
This book explores the integration of Mean Field Game (MFG) theory with machine learning (ML), presenting both theoretical foundations and practical applications. Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including supervised learning, reinforcement learning, and federated learning. MFG theory and ML are converging to address critical challenges in high-dimensional spaces and multi-agent systems. While ML has transformed industries by leveraging vast data and computational power, scalability and robustness remain key concerns. MFG theory, which models large populations of interacting agents, offers a mathematical framework to simplify and optimize complex systems, enhancing ML's efficiency and applicability. By bridging these two fields, this book aims to drive innovation in scalable and robust machine learning. The integration of MFG with ML not only expands research possibilities but also paves the way for more adaptive and intelligent systems. Through this work, the authors hope to inspire further exploration and development in this promising interdisciplinary domain. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG's potential in advancing ML. Industry managers, practitioners and government research workers in the fields of communications and networks will find this book a valuable resource as well.
R 7,268.88
R 434.34
Design of Intelligent Applications using Machine Learning and Deep Learning Techniques
Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL.· Comprehensive analyses and probabilistic approaches for ML and DL.· Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia.· Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals.This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.
R 3,216.46
Handbook of Research on Machine Learning
This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation.The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.
R 3,627.00
Applied Intelligent Decision Making in Machine Learning
The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.
R 7,575.14
Quantum Machine Learning
This text presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, towards improving traditional machine learning algorithms through case-studies.• Covers the core and fundamental aspects of statistics, quantum learning and quantum machines• Discusses the basics of machine learning, regression, supervised and un-supervised machine learning algorithms, and artificial neural networks• Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, boosting.• Introduces quantum evaluation models, deep quantum learning, ensembles and QBoost• Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspectsThe text is primarily written for scholars and researchers working in the fields of Computer Science and Engineering, Information Technology, Electrical Engineering, and Electronics and Communication Engineering.
R 5,644.78
Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications
Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.
R 11,059.24
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e
Through a recent series of 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 capable of learning from data. This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.Use scikit-learn to track an example machine learning project end to endExplore several models, including support vector machines, decision trees, random forests, and ensemble methodsExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detectionDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformersUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learningTrain neural nets using multiple GPUs and deploy them at scale using Google's Vertex AI
R 2,225.00
Machine Learning Hybridization and Optimization for Intelligent Applications
This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation.Features:Focuses on hybridization and optimization of machine learning techniquesReviews supervised, unsupervised, and reinforcement learning using case study-based applicationsCovers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computingExplains computing models using real-world examples and dataset-based experimentsIncludes case study-based explanations and usage for machine learning technologies and applicationsThis book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.
R 3,114.00
Emerging Paradigms in Machine Learning
This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.
R 4,030.00
Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks.Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.
R 7,921.17
Machine Learning in Medical Imaging and Computer Vision
Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.
R 4,417.00
Machine Learning and Security
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
R 1,638.00
Machine Learning under Resource Constraints - Fundamentals
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
R 5,293.00
Artificial Intelligence and Machine Learning
This book focuses on artificial intelligence (AI) and machine learning (ML) technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used.Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT), healthcare, smart cities, robotics, and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances.With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.
R 6,648.72
Machine Learning with Scala Quick Start Guide
Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook DescriptionScala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is forThis book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.
R 1,140.00
Automated Machine Learning with AutoKeras
Create better and easy-to-use deep learning models with AutoKerasKey FeaturesDesign and implement your own custom machine learning models using the features of AutoKerasLearn how to use AutoKeras for techniques such as classification, regression, and sentiment analysisGet familiar with advanced concepts as multi-modal, multi-task, and search space customizationBook DescriptionAutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.What you will learnSet up a deep learning workstation with TensorFlow and AutoKerasAutomate a machine learning pipeline with AutoKerasCreate and implement image and text classifiers and regressors using AutoKerasUse AutoKeras to perform sentiment analysis of a text, classifying it as negative or positiveLeverage AutoKeras to classify documents by topicsMake the most of AutoKeras by using its most powerful extensionsWho this book is forThis book is for machine learning and deep learning enthusiasts who want to apply automated ML techniques to their projects. Prior basic knowledge of Python programming and machine learning is expected to get the most out of this book.
R 1,498.00
Introduction to Machine Learning with Python
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 available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
R 2,076.00
First-order and Stochastic Optimization Methods for Machine Learning
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 learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
R 5,160.71
R 458.00
Lifelong Machine Learning, Second Edition
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 training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
R 2,438.19
Machine Learning with PySpark
Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems.Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You’ll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You’ll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You’ll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark’s latest ML library.After completing this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applicationsWhat you will learn:Build a spectrum of supervised and unsupervised machine learning algorithmsUse PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark’s machine learning libraryUnderstand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit modelsWho This Book Is For Data science and machine learning professionals.
R 1,482.00
Next-Generation Machine Learning with Spark
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications.The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry.Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will LearnBe introduced to machine learning, Spark, and Spark MLlib 2.4.xAchieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM librariesDetect anomalies with the Isolation Forest algorithm for SparkUse the Spark NLP and Stanford CoreNLP libraries that support multiple languagesOptimize your ML workload with the Alluxio in-memory data accelerator for SparkUse GraphX and GraphFrames for Graph AnalysisPerform image recognition using convolutional neural networksUtilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is ForData scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.
R 1,254.00
R 611.00
R 336.00
Machine Learning Paradigms
At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
R 6,122.91
Machine Learning, Optimization, and Data Science
This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020.The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
R 5,101.48
Machine Learning: Concepts, Tools And Data Visualization
This set of lecture notes, written for those who are unfamiliar with mathematics and programming, introduces the reader to important concepts in the field of machine learning. It consists of three parts. The first is an overview of the history of artificial intelligence, machine learning, and data science, and also includes case studies of well-known AI systems. The second is a step-by-step introduction to Azure Machine Learning, with examples provided. The third is an explanation of the techniques and methods used in data visualization with R, which can be used to communicate the results collected by the AI systems when they are analyzed statistically. Practice questions are provided throughout the book.
R 3,957.00
