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Machine Learning
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, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms.In summary, this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms, covers state-of-the-art, practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications.
R 3,114.00
Machine Learning Solutions for Inverse Problems: Part A
Machine Learning Solutions for Inverse Problems: Part A, Volume 26 in the Handbook of Numerical Analysis, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Data-Driven Approaches for Generalized Lasso Problems, Implicit Regularization of the Deep Inverse Prior via (Inertial) Gradient Flow, Generalized Hardness of Approximation, Hallucinations, and Trustworthiness in Machine Learning for Inverse Problems, Energy-Based Models for Inverse Imaging Problems, Regularization Theory of Stochastic Iterative Methods for Solving Inverse Problems, and more.Other sections cover Advances in Identifying Differential Equations from Noisy Data Observations, The Complete Electrode Model for Electrical Impedance Tomography: A Comparative Study of Deep Learning and Analytical Methods, Learned Iterative Schemes: Neural Network Architectures for Operator Learning, Jacobian-Free Backpropagation for Unfolded Schemes with Convergence Guarantees, and Operator Learning Meets Inverse Problems: A Probabilistic Perspective
R 6,768.00
Deep Learning for Natural Language Processing
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways.What You Will LearnGain the fundamentals of deep learning and its mathematical prerequisitesDiscover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classificationWho This Book Is ForSoftware developers who are curious to try out deep learning with NLP.
R 1,596.00
Machine Learning
This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
R 2,561.00
Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
R 5,778.50
Deep Learning in Personalized Music Emotion Recognition
Music has a unique power to evoke strong emotions in us—bringing us to tears, lifting us into ecstasy or triggering vivid memories. Often described as a universal language, it conveys feelings that transcend words. But are machines, too, able to understand this language and capture emotions conveyed in music? This book delves into the field of Musical Emotion Recognition (MER), aiming to develop a mathematical model to predict the emotional content of music. It explores the fundamentals of this interdisciplinary research area, including the relationship between music and emotions, mathematical representations of music and deep learning algorithms. Two MER models are developed and evaluated: one employing handcrafted audio features with a long short-term memory architecture and the other using embeddings from the pre-trained music understanding model MERT. Results show that MERT embeddings can enhance predictions compared to traditional handcrafted features. Additionally, driven by the subjectivity of musical emotions and the low inter-rater agreement of annotations, this book investigates personalized emotion recognition. The findings suggest that personalized models surpass the limitations of general MER systems and can even outperform a theoretically perfect general MER system.
R 3,097.89
R 805.00
R 1,221.00
Generative AI Application Integration Patterns
Unleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.What you will learnConcepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include:Developer engineers with foundational tech knowledgeSoftware architects seeking best practices and design patternsProfessionals using ML for data science, research, etc., who want a deeper understanding of Generative AITechnical product managers with a software development backgroundThis concise focus ensures practical, actionable insights
R 1,693.00
Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design
Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithmsCovers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiencyFocuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.
R 4,297.00
R 519.00
Deep Learning and Practice with MindSpore
This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.
R 5,481.01
Deep Learning Foundations
This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
R 4,677.51
Machine Learning in Farm Animal Behavior using Python
This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis.
R 10,842.80
Learning as a Generative Activity
During the past twenty-five years, researchers have made impressive advances in pinpointing effective learning strategies (namely, activities the learner engages in during learning that are intended to improve learning). In Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding, Logan Fiorella and Richard E. Mayer share eight evidence-based learning strategies that promote understanding: summarizing, mapping, drawing, imagining, self-testing, self-explaining, teaching, and enacting. Each chapter describes and exemplifies a learning strategy, examines the underlying cognitive theory, evaluates strategy effectiveness by analyzing the latest research, pinpoints boundary conditions, and explores practical implications and future directions. Each learning strategy targets generative learning, in which learners actively make sense out of the material so they can apply their learning to new situations. This concise, accessible introduction to learning strategies will benefit students, researchers, and practitioners in educational psychology, as well as general readers interested in the important twenty-first-century skill of regulating one's own learning.
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R 610.00
Mathematics of Deep Learning
The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning" for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.
R 1,596.00
Fiorella & Mayer's Generative Learning in Action
Generative Learning in Action helps to answer the question: which activities can students carry out to create meaningful learning? It does this by considering how we, as teachers, can implement the eight strategies for generative learning set out in the work of Fiorella and Mayer in their seminal 2015 work Learning as a Generative Activity: Eight Learning Strategies that Promote Learning. At a time when a great deal of attention has been paid to the teaching and learning from the perspective of effective instruction, Generative Learning looks at the flip side of coin and considers what is happening in the minds of the learner. This book takes a teachers-eye view of a range of theories of learning and keeps their application to the classroom firmly in mind through the use of case studies and reference to day to day practice. Generative Learning in Action also discusses the key considerations and potential limitations of each of the strategies, as well as how you could implement these in your own practice and more widely across a school. The authors bring a wealth of experience to this topic. Zoe Enser was a classroom English teacher for over 20 years as well as head of department and school leader in charge of improving teaching and learning. She is now lead specialist advisor for Kent with The Education People. Mark Enser has been a geography teacher for the best part of two decades as well as a head of department and research lead. He is the author of Making Every Geography Lesson Count and Teach Like Nobody's Watching as well as a TES columnist.
R 616.00
Deep Reinforcement Learning with Python
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. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is forIf you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you.Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.
R 1,661.00
R 825.00
Java Deep Learning Essentials
Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with JavaAbout This Book• Go beyond the theory and put Deep Learning into practice with Java• Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe• Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep LearningWho This Book Is ForThis book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment.What You Will Learn• Get a practical deep dive into machine learning and deep learning algorithms• Implement machine learning algorithms related to deep learning• Explore neural networks using some of the most popular Deep Learning frameworks• Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms• Discover more deep learning algorithms with Dropout and Convolutional Neural Networks• Gain an insight into the deep learning library DL4J and its practical uses• Get to know device strategies to use deep learning algorithms and libraries in the real world• Explore deep learning further with Theano and CaffeIn DetailAI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset.Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you'll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today.By the end of the book, you'll be ready to tackle Deep Learning with Java. Wher
R 1,889.00
Production-Ready Applied Deep Learning
Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud servicesKey FeaturesUnderstand how to execute a deep learning project effectively using various tools availableLearn how to develop PyTorch and TensorFlow models at scale using Amazon Web ServicesExplore effective solutions to various difficulties that arise from model deploymentBook DescriptionMachine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives.First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors’ collective knowledge of deploying hundreds of AI-based services at a large scale.By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting.What you will learnUnderstand how to develop a deep learning model using PyTorch and TensorFlowConvert a proof-of-concept model into a production-ready applicationDiscover how to set up a deep learning pipeline in an efficient way using AWSExplore different ways to compress a model for various deployment requirementsDevelop Android and iOS applications that run deep learning on mobile devicesMonitor a system with a deep learning model in productionChoose the right system architecture for developing and deploying a modelWho this book is forMachine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.
R 1,791.00
Machine Learning Methods for Multi-Omics Data Integration
The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data.Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.
R 9,158.00
Machine Learning Contests: A Guidebook
This book systematically introduces the competitions in the field of algorithm and machine learning. The first author of the book has won 5 championships and 5 runner-ups in domestic and international algorithm competitions.Firstly, it takes common competition scenarios as a guide by giving the main processes of using machine learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover, this book comprehensively covers several common problems in the field of machine learning competitions such as recommendation, temporal prediction, advertising, text computing, etc.The authors, also knew as "competition professionals”, will explain the actual cases in detail and teach you various processes, routines, techniques and strategies, which is a rare treasure book for all competition enthusiasts. It is very suitable for readers who are interested in algorithm competitions and deep learning algorithms in practice, or computer-related majors.
R 2,198.00
Practical Guide to Oracle AI Engineering
Learn how to build scalable data pipelines, train and deploy ML models, and deliver intelligent applications by leveraging the full power of Oracle's machine learning and GenAI services across cloud and database ecosystems.Key FeaturesApply practical data engineering methods to create intelligent enterprise applicationsMaster in-database ML, vectors, RAG, and GenAI agents through real-world examplesLearn about the ethics and security implications of AI technologyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn A Practical Guide to Oracle AI Engineering, you'll learn how to tackle the challenges of building scalable, high-performance AI workflows in modern enterprises. Many organizations struggle to turn raw data into actionable insights while maintaining security, compliance, and operational efficiency. This book provides practical, end-to-end guidance for data engineers and architects to design, secure, implement, and optimize ML and GenAI solutions across Oracle Cloud, Oracle Database, and MySQL HeatWave.Written by multiple Oracle experts with deep experience in Oracle technologies and enterprise data platforms, this book walks you through real-world examples and hands-on workflows, from data preparation and in-database ML to deploying GenAI-powered applications and intelligent agents. You’ll gain skills in building pipelines, managing models, leveraging vector search for advanced AI use cases, and integrating AI into business applications with APEX and Oracle Digital Assistant. Advanced topics include scalable model deployment, serverless inference, monitoring, and MLOps best practices. By the end, you’ll be equipped to solve complex data challenges, accelerate AI adoption, and deliver measurable business impact through intelligent, production-ready solutions.What you will learnBuild scalable data pipelines for AI and ML workflowsPrepare and engineer data efficiently for in-database MLTrain, optimize, and deploy ML models across Oracle platformsUse GenAI and RAG-enabled GenAI agents for intelligent applicationsIntegrate AI vector search for semantic retrieval and recommendationsImplement ML inside the database, for improved performance and data currencyEnhance business applications with AI using APEX and Oracle Digital AssistantApply best practices for MLOps, monitoring, and secure AI workflowsWho this book is forThis book is for data engineers, architects, IT specialists, and data leaders responsible for building, managing, and optimizing enterprise data solutions. If you face challenges in designing secure, scalable pipelines or deploying ML and GenAI applications, this guide provides practical workflows and real-world strategies to accelerate AI adoption.
R 1,628.00
Art and Ethics of Generative AI
Generative AI technologies are reshaping the way we create, communicate, and compute, presenting both unprecedented opportunities and profound challenges. As these systems increasingly influence decisions that shape our lives, it becomes clear that performance metrics alone cannot define their success. A deeper understanding of the intersection between technical innovation and human values is essential to navigate this transformative era.The Art and Ethics of Generative AI: Fairness, Transparency, and Human Values provides a balanced exploration of generative AI, combining technical insights with ethical considerations. It explains generative model architectures and learning paradigms in an accessible way, while addressing critical issues such as algorithmic bias, explainability challenges, and emerging regulatory approaches. Practical frameworks for responsible AI development are offered, alongside multidisciplinary perspectives that bridge technical and humanistic viewpoints. Abstract concepts are brought to life through practical illustrations, demonstrating how ethical considerations play out in real-world AI applications.Designed for a wide audience, the book serves AI practitioners seeking actionable guidance for building responsible systems, researchers exploring nuanced ethical dimensions, educators and students looking for comprehensive learning materials, and policymakers needing governance insights. It is an essential resource for anyone engaged in the rapidly evolving AI ecosystem.
R 5,165.00
Recent Trends in Learning From Data
This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.
R 5,129.12
R 611.00
Federated Learning
As data becomes more abundant and widespread across personal devices, the need for secure, privacy-aware machine learning is growing. Federated Learning (FL) offers a promising solution, enabling smart devices to collaboratively train models without sharing raw data. Yet, despite its benefits, FL faces serious risks from poisoning and inference attacks.This book begins by introducing the fundamentals of machine learning, along with core deep learning architectures. Based on this foundation, it introduces the concept of Federated Learning (FL), which is a decentralised approach that enables collaborative model training without sharing raw data. The book provides an in-depth exploration of FL’s various forms, system architectures, and practical applications. A significant emphasis is placed on the growing security and privacy concerns in FL, particularly poisoning (both data poisoning and model poisoning) and inference attacks. It discusses state-of-the-art mitigation strategies, such as Byzantine-robust aggregation and inference-resistant techniques, supported with practical implementation insights.This book uniquely bridges foundational concepts with advanced topics in Federated Learning, offering a comprehensive view of its vulnerabilities and their mitigation. By combining theory with practical implementation of attacks and mitigation techniques, it serves as a valuable resource for researchers, practitioners, and students aiming to build secure, privacy-preserving collaborative machine learning systems.This book is unique due to its end-to-end coverage of Federated Learning (FL), from foundational machine and deep learning concepts to real-time deployment of FL along with security and privacy challenges associated. It both explains theory and offers hands-on implementation of attacks and defenses. This practical approach, combined with a clear structure and real-world relevance, makes it ideal for both academic and industry audiences. Promotional emphasis should highlight the book’s focus on actionable insights, its relevance to privacy-preserving and secure AI, and its utility as a learning and reference tool for building secure collaborative learning systems.
R 3,608.83
Deep Learning with PyTorch Workshop
Get a head start in the world of AI and deep learning by developing your skills with PyTorchKey FeaturesLearn how to define your own network architecture in deep learningImplement helpful methods to create and train a model using PyTorch syntaxDiscover how intelligent applications using features like image recognition and speech recognition really process your dataBook DescriptionWant to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch.It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures.The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.What you will learnExplore the different applications of deep learningUnderstand the PyTorch approach to building neural networksCreate and train your very own perceptron using PyTorchSolve regression problems using artificial neural networks (ANNs)Handle computer vision problems with convolutional neural networks (CNNs)Perform language translation tasks using recurrent neural networks (RNNs)Who this book is forThis deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.
R 1,335.00
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
Machine Learning and Knowledge Discovery in Databases
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.The volumes are organized in topical sections as follows:Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
R 3,952.43
Automated Design of Machine Learning and Search Algorithms
This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
R 4,809.94
Machine Learning and Knowledge Discovery in Databases
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.The volumes are organized in topical sections as follows:Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
R 2,851.09
R 611.00
Graph Learning and Network Science for Natural Language Processing
Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models.Features:Presents a comprehensive study of the interdisciplinary graphical approach to NLPCovers recent computational intelligence techniques for graph-based neural network modelsDiscusses advances in random walk-based techniques, semantic webs, and lexical networksExplores recent research into NLP for graph-based streaming dataReviews advances in knowledge graph embedding and ontologies for NLP approachesThis book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.
R 2,796.74
Machine Learning and Knowledge Discovery in Databases
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.The volumes are organized in topical sections as follows:Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
R 5,487.25
Efficient Learning Machines
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions.Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms.Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
R 1,163.23
R 333.00
Deep Learning Innovations and Their Convergence With Big Data
The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics.Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection
R 7,931.55
Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance
If a manufacturing company's main goal is to sell products profitably, protecting production systems from defects is essential and has led to vast documentation and expert knowledge. Industry 4.0 has facilitated access to sensor and operational data across the shop floor, enabling data-driven models that detect faults and predict failures, which are crucial for predictive maintenance to minimize unplanned downtimes and costs. Commonly, a universally applicable machine learning (ML) approach is used without explicitly integrating prior knowledge from sources beyond training data, risking incorrect rediscovery or neglecting already existing knowledge. Integrating expert knowledge with ML can address the scarcity of failure examples and avoid the learning of spurious correlations, though it poses technical challenges when combining Semantic Web-based knowledge graphs with neural networks (NNs) for time series data.For his research, a physical smart factory model with condition monitoring sensors and a knowledge graph was developed. This setup generated the required data for exploring the integration of expert knowledge with (Siamese) NNs for similarity-based fault detection, anomaly detection, and automation of root cause analysis. Patrick Klein applied symbolic and sub-symbolic AI techniques, demonstrating that integrating expert knowledge with NNs enhances prediction performance and confidence in them while reducing the number of learnable parameters and failure examples.
R 4,396.00
