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R 439.00
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
R 1,536.86
R 763.00
Machine Learning, Deep Learning, and Blockchain
This book constitutes the refereed proceedings of Fourth International Research Conference on Machine Learning, Deep Learning, and Blockchain, IRCICD 2023, held in Chennai, India, during April 28–29, 2023. The 9 full papers presented were carefully reviewed and selected from 147 submissions. They were organized in topical sections as follows: machine learning; deep learning; and block chain.
R 4,626.82
Hands-On Deep Learning for Images with TensorFlow
Explore TensorFlow's capabilities to perform efficient deep learning on imagesKey FeaturesDiscover image processing for machine visionBuild an effective image classification system using the power of CNNsLeverage TensorFlow’s capabilities to perform efficient deep learningBook DescriptionTensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.What you will learnBuild machine learning models particularly focused on the MNIST digitsWork with Docker and Keras to build an image classifierUnderstand natural language models to process text and imagesPrepare your dataset for machine learningCreate classical, convolutional, and deep neural networksCreate a RESTful image classification serverWho this book is forHands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
R 912.00
Deep Learning Revolution
How deep learning from Google Translate to driverless cars to personal cognitive assistants is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
R 638.00
Scalable and Distributed Machine Learning and Deep Learning Patterns
Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.
R 10,420.70
Handbook of Deep Learning Applications
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
R 8,552.21
R 764.00
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 6,960.00
R 766.92
Mathematical Aspects of Deep Learning
In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.
R 3,460.00
Artificial Intelligence, Machine Learning, and Deep Learning
This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learningclassifiers such as logistic regression, k-NN, decision trees, random forests,and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs,LSTMs, and auto encoders. Keras-based code samples are included to supplementthe theoretical discussion. In addition, this book contains appendices forKeras, TensorFlow 2, and Pandas.
R 1,527.00
Advances in Neural Computation, Machine Learning, and Cognitive Research IX
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXVII International Conference on Neuroinformatics, held on October 20–24, 2025, in Moscow, Russia.
R 13,103.15
Machine Learning and Deep Learning Using Python and TensorFlow
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Explore the principles and practices of machine learning and deep learning This comprehensive textbook lays out the theories and applications of machine learning and deep learning in a style that is approachable for students and working professionals at all math skill levels. You will discover how to handle data, regression and logistic regression, decision trees, cross-validation techniques and error testing, artificial neural networks (ANN, CNN and RNN), random forests, boosting, and more. Machine Learning and Deep Learning Using Python and TensorFlow includes valuable case studies across professional domains, including banking, insurance, e-commerce, retail, and healthcare. The book presents examples using both Python and TensorFlow programming languages and clearly demonstrates how to utilize this cutting-edge technology in today’s “smart” devices.• Presented in a mathematically non-rigorous way• Includes image classification and computer vision tasks • Written by a pair of business analytics experts
R 4,616.00
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMUThis book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYUThis is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors!--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level.Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist.Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group
R 4,563.08
Machine Learning for Causal Inference
This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
R 6,594.00
Deep Learning in Genome Mapping
The book aims to develop methodologies, and throw light on empirical research advances of various machine learning systems through data mining and parallel programming using GPU approaches. It reviews concepts of existing machine learning and deep learning techniques and how these can be implemented in GPU computing with CUDA architecture. The book also discusses modern machine learning techniques for effective big data management in accordance with worldwide standards in the field.Covering diverse areas, this publication is meant for academicians, data scientists, industrial professionals, researchers and students interested in uncovering the latest innovations in the field.
R 9,772.69
Advanced Applied Deep Learning
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.What You Will LearnSee how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is ForScientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.
R 1,026.00
Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing.Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
R 10,437.19
Deep Learning in Biometrics
Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start-ups are focusing on deep learning-based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. This book covers topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoencoders. The focus is also on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints, while examining the future trends in deep learning and biometric research. Contains chapters written by authors who are leading researchers in biometrics. Presents a comprehensive overview on the internal mechanisms of deep learning. Discusses the latest developments in biometric research. Examines future trends in deep learning and biometric research. Provides extensive references at the end of each chapter to enhance further study.
R 9,324.22
R 611.00
Deep Learning Theory and Applications
This book constitutes the refereed post-conference proceedings of the Third International Conference on Deep Learning Theory and Applications, DeLTA 2022, held in Lisbon, Portugal, during January 17-18, 2022.The 6 full papers included in this book were carefully reviewed and selected from 36 submissions. They present recent research on machine learning and artificial intelligence in real-world applications such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains.
R 2,718.74
Deep Learning in Mining of Visual Content
This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to computer-aided diagnostics of Alzheimer’s disease on multimodal imaging. This book targets advanced-level students studying computer science including computer vision, data analytics and multimedia. Researchers and professionals working in computer science, signal and image processing may also be interested in this book.
R 2,221.31
Deep Learning for Physical Scientists
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptronsAn exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of trainingAn examination of multi-layer perceptrons for learning from descriptors and de-noising dataDiscussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from imagesA treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access.
R 2,176.00
Multi-faceted Deep Learning
This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
R 6,002.77
Computational Methods for Deep Learning
The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
R 3,663.00
R 365.00
Supervised Learning
This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the output and input variables, where said learning is undertaken within the remit of real-world information that can be messy in different ways. For example, the available data may be highly multivariate or be high-dimensional, reflecting the nature of the output variable that could be a vector, or matrix, or even higher in dimension, as is often the case in a real-world application. Additionally, the data is noisy, and often it is small to moderately large in size in multiple applications. Another difficulty that regularly arises is that the training dataset – comprising pairs of values of the input and output –is such, that the sought function cannot be captured by a parametric shape, but is instead underlined by an inhomogeneous correlation structure. These difficulties notwithstanding, we desire a streamlined methodology that allows the learning of the inter variable relationship – to ultimately permit fast and reliable predictions of the output, at newly recorded values of the input. In fact, occasions arise when one seeks values of the input at which a new output value is recorded, and such a demand is also addressed in the book.The generic solution to the problem of secured supervised learning amidst real-world messiness, lies in treating the sought inter-variable relation as a (function-valued) random variable, which, being random, is ascribed a probability distribution. Then recalling that distributions on the space of functions are given by stochastic processes, the sought function is proposed to be a sample function of a stochastic process. This process is chosen as one that imposes minimal constraints on the sought function – identified as a Gaussian Process (GP) in the book. Thus, the sought function can be inferred upon, as long as the co-variance function of the underlying GP is learnt, given the available training set. The book presents probabilistic techniques to undertake said learning, within the challenges borne by the data, and illustrates such techniques on real data. Learning of a function is always followed by closed-form prediction of the mean and dispersion of the output variable that is realised at a test input. To help with the background, the book includes reviews on stochastic processes and basic probability theory. This will render the first half of the book useful for students across disciplines, while the latter half will be appreciated by students of numerate subjects at the postgraduate level or higher, including students of computational sciences, statistics and mathematics.
R 7,588.22
Enhancing Deep Learning with Bayesian Inference
Develop Bayesian Deep Learning models to help make your own applications more robust.Key FeaturesGain insights into the limitations of typical neural networksAcquire the skill to cultivate neural networks capable of estimating uncertaintyDiscover how to leverage uncertainty to develop more robust machine learning systemsBook DescriptionDeep learning is revolutionizing our lives, impacting content recommendations and playing a key role in mission- and safety-critical applications. Yet, typical deep learning methods lack awareness about uncertainty. Bayesian deep learning offers solutions based on approximate Bayesian inference, enhancing the robustness of deep learning systems by indicating how confident they are in their predictions. This book will guide you in incorporating model predictions within your applications with care.Starting with an introduction to the rapidly growing field of uncertainty-aware deep learning, you’ll discover the importance of uncertainty estimation in robust machine learning systems. You’ll then explore a variety of popular Bayesian deep learning methods and understand how to implement them through practical Python examples covering a range of application scenarios.By the end of this book, you’ll embrace the power of Bayesian deep learning and unlock a new level of confidence in your models for safer, more robust deep learning systems.What you will learnDiscern the advantages and disadvantages of Bayesian inference and deep learningBecome well-versed with the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations and approximationsRecognize the merits of probabilistic DNNs in production contextsMaster the implementation of a variety of BDL methods in Python codeApply BDL methods to real-world problemsEvaluate BDL methods and choose the most suitable approach for a given taskDevelop proficiency in dealing with unexpected data in deep learning applicationsWho this book is forThis book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You’re expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models.
R 2,052.00
MATLAB Deep Learning
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.What You'll LearnUse MATLAB for deep learningDiscover neural networks and multi-layer neural networksWork with convolution and pooling layersBuild a MNIST example with these layersWho This Book Is ForThose who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
R 2,230.13
10 Machine Learning Blueprints You Should Know for Cybersecurity
Work on 10 practical projects, each with a blueprint for a different machine learning technique, and apply them in the real world to fight against cybercrimePurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn how to frame a cyber security problem as a machine learning problemExamine your model for robustness against adversarial machine learningBuild your portfolio, enhance your resume, and ace interviews to become a cybersecurity data scientistBook DescriptionMachine learning in security is harder than other domains because of the changing nature and abilities of adversaries, high stakes, and a lack of ground-truth data. This book will prepare machine learning practitioners to effectively handle tasks in the challenging yet exciting cybersecurity space.The book begins by helping you understand how advanced ML algorithms work and shows you practical examples of how they can be applied to security-specific problems with Python – by using open source datasets or instructing you to create your own. In one exercise, you’ll also use GPT 3.5, the secret sauce behind ChatGPT, to generate an artificial dataset of fabricated news. Later, you’ll find out how to apply the expert knowledge and human-in-the-loop decision-making that is necessary in the cybersecurity space. This book is designed to address the lack of proper resources available for individuals interested in transitioning into a data scientist role in cybersecurity. It concludes with case studies, interview questions, and blueprints for four projects that you can use to enhance your portfolio.By the end of this book, you’ll be able to apply machine learning algorithms to detect malware, fake news, deep fakes, and more, along with implementing privacy-preserving machine learning techniques such as differentially private ML.What you will learnUse GNNs to build feature-rich graphs for bot detection and engineer graph-powered embeddings and featuresDiscover how to apply ML techniques in the cybersecurity domainApply state-of-the-art algorithms such as transformers and GNNs to solve security-related issuesLeverage ML to solve modern security issues such as deep fake detection, machine-generated text identification, and stylometric analysisApply privacy-preserving ML techniques and use differential privacy to protect user data while training ML modelsBuild your own portfolio with end-to-end ML projects for cybersecurityWho this book is forThis book is for machine learning practitioners interested in applying their skills to solve cybersecurity issues. Cybersecurity workers looking to leverage ML methods will also find this book useful. An understanding of the fundamental machine learning concepts and beginner-level knowledge of Python programming are needed to grasp the concepts in this book. Whether you’re a beginner or an experienced professional, this book offers a unique and valuable learning experience that’ll help you develop the skills needed to protect your network
R 1,693.00
R 1,801.00
Hands-On Transfer Learning with Python
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.What you will learnSet up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL modelsExplore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functionsGet to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLPWho this book is forHands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
R 1,661.00
Deep Learning through Sparse and Low-Rank Modeling
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
R 2,941.00
Keras Deep Learning Cookbook
Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learningWhat you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras library Understand the different Keras layers Use Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.
R 1,335.00
Supply Chain Transformation Through Generative AI and Machine Learning
The transformative role of Generative Artificial Intelligence (AI) and Machine Learning (ML) in supply chain management is increasingly being recognized as a game-changer in the industry. Recent statistics underscore this trend, highlighting the rapid adoption and significant impact of these technologies. However, the path to digital transformation is not without its challenges. Despite improved success rates, about 60% of digital transformation initiatives in supply chains still struggle to fully meet their objectives. This shortfall is often attributed to several key factors: the complexity and scale of integrating new technologies into existing systems; organizational resistance to change and inadequate stakeholder buy-in; lack of skilled professionals adept in these new technologies; insufficient data governance and quality; and underestimation of the need for a robust change management strategy. These challenges highlight the critical need for a comprehensive approach that addresses both the technical and human aspects of digital transformation. Supply Chain Transformation Through Generative AI and Machine Learning is a comprehensive resource to the best practices in digital enablement, change management, and process optimization, with a specific focus on Generative AI and ML. It equips readers with the knowledge and strategies necessary for successful integration of these technologies, drawing on the latest industry insights and expert recommendations, to enhance supply chain efficiency and effectiveness, reduce costs, and drive revenue growth. Covering topics such as AI-powered visual models, demand planning, and product clustering, this book is an excellent resource for executives, business leaders, program managers, data scientists, AI and ML developers, industry analysts, consultants, professionals, scholars, researchers, academicians, and more.
R 8,766.62
Combating Women's Health Issues with Machine Learning
The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers.The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.
R 2,990.47
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
Practical Deep Learning at Scale with MLflow
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey FeaturesFocus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityBook DescriptionThe book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.What you will learnUnderstand MLOps and deep learning life cycle developmentTrack deep learning models, code, data, parameters, and metricsBuild, deploy, and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is forThis book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
R 1,596.00
