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Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing
This two-volume set CCIS 2490-2491 constitutes the refereed proceedings of the Third International Conference on Paradigm Shifts in Communication, Embedded Systems, Machine Learning, and Signal Processing, PCEMS 2024, held in Nagpur, India, during November 11-12, 2024.The 73 full papers and 17 short papers presented in this volume were carefully reviewed and selected from 330 submissions. The papers present recent research in the areas of communication, antenna, computer vison, medical image analysis, deep learning, AI based systems and applications, classification problem, embedded system and IoT, etc.
R 8,504.58
Advanced Deep Learning with Python
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is forThis book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
R 1,661.00
Probability on Compact Lie Groups
Probability theory on compact Lie groups deals with the interaction between “chance” and “symmetry,” a beautiful area of mathematics of great interest in its own sake but which is now also finding increasing applications in statistics and engineering (particularly with respect to signal processing). The author gives a comprehensive introduction to some of the principle areas of study, with an emphasis on applicability. The most important topics presented are: the study of measures via the non-commutative Fourier transform, existence and regularity of densities, properties of random walks and convolution semigroups of measures and the statistical problem of deconvolution. The emphasis on compact (rather than general) Lie groups helps readers to get acquainted with what is widely seen as a difficult field but which is also justified by the wealth of interesting results at this level and the importance of these groups for applications.The book is primarily aimed at researchers working in probability, stochastic analysis and harmonic analysis on groups. It will also be of interest to mathematicians working in Lie theory and physicists, statisticians and engineers who are working on related applications. A background in first year graduate level measure theoretic probability and functional analysis is essential; a background in Lie groups and representation theory is certainly helpful but the first two chapters also offer orientation in these subjects.
R 3,308.68
Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication
This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.
R 8,274.62
Structural Theory of Probability
The Structural Theory of Probability addresses the interpretation of probability, often debated in the scientific community. This problem has been examined for centuries; perhaps no other mathematical calculation suffuses mankind's efforts at survival as amply as probability. In the dawn of the 20th century David Hilbert included the foundations of the probability calculus within the most vital mathematical problems; Dr. Rocchi's topical and ever-timely volume proposes a novel, exhaustive solution to this vibrant issue.Paolo Rocchi, a versatile IBM scientist, outlines a new philosophical and mathematical approach inspired by well-tested software techniques. Through the prism of computer technology he provides an innovative view on the theory of probability. Dr. Rocchi discusses in detail the mathematical tools used to clarify the meaning of probability, integrating with care numerous examples and case studies. The comprehensiveness and originality of its mathematical development make this volume an inspiring read for researchers and students alike.
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Deep Learning: Concepts and Architectures
This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.
R 6,594.00
Science of Deep Learning
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
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Python Deep Learning Projects
Insightful projects to master deep learning and neural network architectures using Python and KerasKey FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient wayWhat you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is forPython Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects.It is assumed that you have sound knowledge of Python programming
R 1,661.00
Topics In Probability
Recent research in probability has been concerned with applications such as data mining and finance models. Some aspects of the foundations of probability theory have receded into the background. Yet, these aspects are very important and have to be brought back into prominence.
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Deep Learning Models for Medical Imaging
Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.
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Elementary Probability
Now available in a fully revised and updated second edition, this well established textbook provides a straightforward introduction to the theory of probability. The presentation is entertaining without any sacrifice of rigour; important notions are covered with the clarity that the subject demands. Topics covered include conditional probability, independence, discrete and continuous random variables, basic combinatorics, generating functions and limit theorems, and an introduction to Markov chains. The text is accessible to undergraduate students and provides numerous worked examples and exercises to help build the important skills necessary for problem solving.
R 3,855.26
Deep Reinforcement Learning in Unity
Gain an in-depth overview of reinforcement learning for autonomous agents in game development with Unity.This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML Agents Toolkit (including how to install and set up ML agents from the GitHub repository), and installing fundamental machine learning libraries and frameworks (such as Tensorflow). You will learn about: deep learning and work through an introduction to Tensorflow for writing neural networks (including perceptron, convolution, and LSTM networks), Q learning with Unity ML agents, and porting trained neural network models in Unity through the Python-C# API. You will also explore the OpenAI Gym Environment used throughout the book.Deep Reinforcement Learning in Unity provides a walk-through of the core fundamentals of deep reinforcement learning algorithms, especially variants of the value estimation, advantage, and policy gradient algorithms (including the differences between on and off policy algorithms in reinforcement learning). These core algorithms include actor critic, proximal policy, and deep deterministic policy gradients and its variants. And you will be able to write custom neural networks using the Tensorflow and Keras frameworks. Deep learning in games makes the agents learn how they can perform better and collect their rewards in adverse environments without user interference. The book provides a thorough overview of integrating ML Agents with Unity for deep reinforcement learning.What You Will Learn Understand how deep reinforcement learning works in gamesGrasp the fundamentals of deep reinforcement learning Integrate these fundamentals with the Unity ML Toolkit SDKGain insights into practical neural networks for training Agent Brain in the context of Unity ML AgentsCreate different models and perform hyper-parameter tuningUnderstand the Brain-Academy architecture in Unity ML AgentsUnderstand the Python-C# API interface during real-time training of neural networksGrasp the fundamentals of generic neural networks and their variants using TensorflowCreate simulations and visualize agents playing games in UnityWho This Book Is ForReaders with preliminary programming and game development experience in Unity, and those with experience in Python and a general idea of machine learning
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R 1,208.42
Trends in Deep Learning Methodologies
Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
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Invitation to Probability and Data Analysis for Physicists
This book is an introduction to probability, statistics, data analysis, and hypothesis testing in physics. It is designed to be taught at different levels, from basic to advanced, and to be enjoyed by anyone doing research in the physical sciences or related subjects. It aims to be a companion in a journey leading the reader to appreciate and enjoy the major role of these subjects in all aspects of the physical sciences. With specific examples, the book encourages readers to meditate on the distinctive meaning of probability in classical and quantum physics. The author draws on his teaching experience in statistical physics and data analysis, as well as his research work in fields like complex systems and soft matter, to provide a comprehensive introduction to statistical methods.
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Synthetic Data for Deep Learning
This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.
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Hands-On Deep Learning with Go
Apply modern deep learning techniques to build and train deep neural networks using GorgoniaKey FeaturesGain a practical understanding of deep learning using GolangBuild complex neural network models using Go libraries and GorgoniaTake your deep learning model from design to deployment with this handy guideBook DescriptionGo is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems.What you will learnExplore the Go ecosystem of libraries and communities for deep learningGet to grips with Neural Networks, their history, and how they workDesign and implement Deep Neural Networks in GoGet a strong foundation of concepts such as Backpropagation and MomentumBuild Variational Autoencoders and Restricted Boltzmann Machines using GoBuild models with CUDA and benchmark CPU and GPU modelsWho this book is forThis book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.
R 1,661.00
Accelerate Deep Learning Workloads with Amazon SageMaker
Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance.Key FeaturesExplore key Amazon SageMaker capabilities in the context of deep learningTrain and deploy deep learning models using SageMaker managed capabilities and optimize your deep learning workloadsCover in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMakerBook DescriptionOver the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep-learning tasks, such as computer vision and natural language processing. You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open-source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads.By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker.What you will learnCover key capabilities of Amazon SageMaker relevant to deep learning workloadsOrganize SageMaker development environmentPrepare and manage datasets for deep learning trainingDesign, debug, and implement the efficient training of deep learning modelsDeploy, monitor, and optimize the serving of DL modelsWho this book is forThis book is relevant for ML engineers who work on deep learning model development and training, and for Solutions Architects who design and optimize end-to-end deep learning workloads. It assumes familiarity with the Python ecosystem, principles of Machine Learning and Deep Learning, and basic knowledge of the AWS cloud.
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Neural Networks for Conditional Probability Estimation
Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5.
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Guide to Deep Learning Basics
This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsú László, and Geoffrey Hinton.Topics and features:Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AIPresents a philosophical case for the use of fuzzy logic approaches in AIInvestigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguisticsExamines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferencesDebates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living beingInvestigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AIExplores philosophical questions at the intersection of AI and transhumanismThis inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.
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Probability and Random Variables
This is a simple and concise introduction to probability theory. Self-contained and readily accessible, it is written in an informal tutorial style with concepts and techniques defined and developed as necessary. After an elementary discussion of chance, the central and crucial rules and ideas of probability including independence and conditioning are set out. Examples, demonstrations, and exercises are used throughout to explore the ways in which probability is motivated by, and applied to, real life problems in science, medicine, gaming and other subjects of interest. This book is suitable for students taking introductory courses in probability and will provide a solid foundation for more advanced courses in probability and statistics. It would also be a valuable reference to those needing a working knowledge of probability theory and will appeal to anyone interested in this endlessly fascinating and entertaining subject.
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R 916.00
Introduction to Deep Reinforcement Learning
The current era of artificial intelligence and machine learning (AIML) tools has transformed the workings of vast swaths of our private, working, and social lives beyond recognition. It has been found that they can solve many problems in better and faster ways compared to humans. AIML tools allow machines and related systems to reason and infer almost like humans, and this has deep intellectual and philosophical ramifications as well. The areas of machine learning are broadly classified into supervised, unsupervised, and deep reinforcement learning (DRL). The last one comes closest to how humans reason, and various innovations in this area have many useful applications.This book covers most of the areas of DRL with a special focus on its mathematical and algorithmic foundations. Undergraduate and early graduate students should find it to be a good guide to the fast-developing areas of DRL and its myriad applications. Hopefully, it will spur them to dive deep and understand the coming revolution in every aspect of society based on these ideas.
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Noncommutative Probability
The intention of this book is to explain to a mathematician having no previous knowledge in this domain, what "noncommutative probability" is. So the first decision was not to concentrate on a special topic. For different people, the starting points of such a domain may be different. In what concerns this question, different variants are not discussed. One such variant comes from Quantum Physics. The motivations in this book are mainly mathematical; more precisely, they correspond to the desire of developing a probability theory in a new set-up and obtaining results analogous to the classical ones for the newly defined mathematical objects. Also different mathematical foundations of this domain were proposed. This book concentrates on one variant, which may be described as "von Neumann algebras". This is true also for the last chapter, if one looks at its ultimate aim. In the references there are some papers corresponding to other variants; we mention Gudder, S.P. &al (1978). Segal, I.E. (1965) also discusses "basic ideas".
R 3,049.23
Deep Learning And Signal Processing
This monograph provides a comprehensive synthesis of recent research that demonstrates how deep learning methods can be effectively applied to achieve technological innovation in signal processing.Deep Learning and Signal Processing takes the reader through the evolution of machine and deep learning, beginning with the foundations of neural networks to advanced architectures such as deep recursive cascaded convolutional neural networks (CNNs), and core deep learning principles, to cutting-edge applications. Practical case studies in multiple areas such as blind equalization, remote sensing image classification, motion deblurring, handwriting recognition, ADHD diagnosis, and more, are integrated with learning points throughout the book.By bridging theory with real-world applications, the book equips engineering technicians, postgraduate students, researchers, and AI professionals with the expertise to overcome technological bottlenecks in applying deep learning to signal processing within their respective domains.
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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging
This book constitutes the refereed proceedings of the 7th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2025, held in conjunction with MICCAI 2025, in Daejon, South Korea, on September 27, 2025. The 22 full papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: Risk management, uncertainty interpretation and visualisation; domain shift and out-of-distribution management; uncertainty calibration; and uncertainty modelling and estimation, Bayesian deep learning.
R 2,548.92
Deep Learning Crash Course
Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.
R 1,669.00
EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII
This book comprises nine selected works on numerical and computational methods for solving multiobjective optimization, game theory, and machine learning problems. It provides extended versions of selected papers from various fields of science such as computer science, mathematics and engineering that were presented at EVOLVE 2013 held in July 2013 at Leiden University in the Netherlands. The internationally peer-reviewed papers include original work on important topics in both theory and applications, such as the role of diversity in optimization, statistical approaches to combinatorial optimization, computational game theory, and cell mapping techniques for numerical landscape exploration. Applications focus on aspects including robustness, handling multiple objectives, and complex search spaces in engineering design and computational biology.
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Deep Learning For Physics Research
A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
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State-of-the-Art Deep Learning Models in TensorFlow
Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks.The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning.Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office.What You Will LearnTake advantage of the built-in support of the Google Colab ecosystemWork with TensorFlow data setsCreate input pipelines to feed state-of-the-art deep learning modelsCreate pipelined state-of-the-art deep learning models with clean and reliable Python codeLeverage pre-trained deep learning models to solve complex machine learning tasksCreate a simple environment to teach an intelligent agent to make automated decisionsWho This Book Is ForReaders who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab
R 1,824.00
Asymptotic Approximations for Probability Integrals
This book gives a self-contained introduction to the subject of asymptotic approximation for multivariate integrals for both mathematicians and applied scientists. A collection of results of the Laplace methods is given. Such methods are useful for example in reliability, statistics, theoretical physics and information theory. An important special case is the approximation of multidimensional normal integrals. Here the relation between the differential geometry of the boundary of the integration domain and the asymptotic probability content is derived. One of the most important applications of these methods is in structural reliability. Engineers working in this field will find here a complete outline of asymptotic approximation methods for failure probability integrals.
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Deep Learning and Parallel Computing Environment for Bioengineering Systems
Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas.
R 4,708.00
Computational Probability with Python
Probability theory provides the foundation for understanding uncertainty in fields ranging from finance and insurance to engineering and data science. This textbook is designed for undergraduate actuarial students seeking a clear, practical, and mathematically sound introduction to the subject. Requiring only a first course in calculus, it develops core ideas in a structured and accessible way, making it suitable for a one-semester course as well as for independent study.What sets this book apart is its balanced approach between intuition and rigor. Rather than relying heavily on measure-theoretic formalism or focusing solely on computational techniques, it introduces essential concepts—such as probability spaces—in a gradual and approachable manner. By combining theoretical development with computational tools, the text bridges the gap between abstract understanding and real-world application, preparing students for both academic study and professional practice.Key features:A balanced presentation that introduces foundational concepts of probability without heavy measure-theoretic machineryClear, step-by-step development starting from set theory and building toward probability models and applicationsNumerous practice questions adapted from Society of Actuaries Probability (P) exam materialsSelected solutions with detailed explanations to support self-study and deeper understandingIntegrated Python simulations in every section to illustrate concepts and verify resultsAccompanying Jupyter notebooks available online for interactive learning and experimentationBy combining mathematical rigor, practical exercises, and modern computational tools, this book offers a solid introduction to probability theory. It is ideal for actuarial students preparing for professional exams, while also serving as a valuable resource for students in related fields who seek a solid and engaging foundation in probability.
R 5,165.00
Intermediate Probability
Intermediate Probability is the natural extension of the author's Fundamental Probability. It details several highly important topics, from standard ones such as order statistics, multivariate normal, and convergence concepts, to more advanced ones which are usually not addressed at this mathematical level, or have never previously appeared in textbook form. The author adopts a computational approach throughout, allowing the reader to directly implement the methods, thus greatly enhancing the learning experience and clearly illustrating the applicability, strengths, and weaknesses of the theory. The book: Places great emphasis on the numeric computation of convolutions of random variables, via numeric integration, inversion theorems, fast Fourier transforms, saddlepoint approximations, and simulation.Provides introductory material to required mathematical topics such as complex numbers, Laplace and Fourier transforms, matrix algebra, confluent hypergeometric functions, digamma functions, and Bessel functions.Presents full derivation and numerous computational methods of the stable Paretian and the singly and doubly non-central distributions.A whole chapter is dedicated to mean-variance mixtures, NIG, GIG, generalized hyperbolic and numerous related distributions.A whole chapter is dedicated to nesting, generalizing, and asymmetric extensions of popular distributions, as have become popular in empirical finance and other applications.Provides all essential programming code in Matlab and R. The user-friendly style of writing and attention to detail means that self-study is easily possible, making the book ideal for senior undergraduate and graduate students of mathematics, statistics, econometrics, finance, insurance, and computer science, as well as researchers and professional statisticians working in these fields.
R 6,576.00
R Deep Learning Essentials
Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNetKey FeaturesUse R 3.5 for building deep learning models for computer vision and textApply deep learning techniques in cloud for large-scale processingBuild, train, and optimize neural network models on a range of datasetsBook DescriptionDeep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem.This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.What you will learnBuild shallow neural network prediction modelsPrevent models from overfitting the data to improve generalizabilityExplore techniques for finding the best hyperparameters for deep learning modelsCreate NLP models using Keras and TensorFlow in RUse deep learning for computer vision tasksImplement deep learning tasks, such as NLP, recommendation systems, and autoencodersWho this book is forThis second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.
R 1,498.00
Mastering Machine Learning with Python in Six Steps
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
R 1,482.00
Bayesian Machine Learning in Geotechnical Site Characterization
Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization.Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability “degree of belief”, showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion “relative frequency”. It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples.Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.
R 4,165.97
Optimal Learning
Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problemsExtensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problemsAdvanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.
R 4,460.00
Probability, Statistics, and Information Theory for Scientists and Engineers
Probability, Statistics, and Information Theory for Scientists and Engineers offers students clear explanations of the most fundamental and useful statistical concepts. Extensively class-tested, the book is designed to appeal to the intuition, yet provide sound academic support, so that conclusions are clear and unambiguous.The text is a graphical introduction to the subject, which allows for ease of interpretation. In the first sections, students will explore the basic axioms and conclusions of probability and statistics. They will learn what can be expected in data taken from certain specified populations, and conversely, what can be inferred about a population from a data set. Unlike other standard texts, Probability, Statistics, and Information Theory for Scientists and Engineers also includes an introduction to information theory—the study of the structure of probability distributions. This allows for a new and useful perspective on hypothesis testing. Designed for science and engineering students, Probability, Statistics and Information Theory for Scientists and Engineers can be used in courses on engineering, math and statistics, and physics.
R 4,245.88
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
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
