Search results for "Probability for Deep Learning Quantum"
Items we need to order for you
Scalable Monte Carlo for Bayesian Learning
A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.
R 2,400.34
Hands-On Deep Learning for Games
Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop gamesKey FeaturesApply the power of deep learning to complex reasoning tasks by building a Game AIExploit the most recent developments in machine learning and AI for building smart gamesImplement deep learning models and neural networks with PythonBook DescriptionThe number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.What you will learnLearn the foundations of neural networks and deep learning.Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.Working with Unity ML-Agents toolkit and how to install, setup and run the kit.Understand core concepts of DRL and the differences between discrete and continuous action environments.Use several advanced forms of learning in various scenarios from developing agents to testing games.Who this book is forThis books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
R 1,498.00
Python Deep Learning
Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using PythonKey FeaturesUnderstand the theory, mathematical foundations and the structure of deep neural networksBecome familiar with transformers, large language models, and convolutional networksLearn how to apply them on various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe field of deep learning has developed rapidly in the past years and today covers broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.What you will learnEstablish theoretical foundations of deep neural networksUnderstand convolutional networks and apply them in computer vision applicationsBecome well versed with natural language processing and recurrent networksExplore the attention mechanism and transformersApply transformers and large language models for natural language and computer visionImplement coding examples with PyTorch, Keras, and Hugging Face TransformersUse MLOps to develop and deploy neural network modelsWho this book is forThis book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.
R 1,693.00
Foundations of Constructive Probability Theory
Using Bishop's work on constructive analysis as a framework, this monograph gives a systematic, detailed and general constructive theory of probability theory and stochastic processes. It is the first extended account of this theory: almost all of the constructive existence and continuity theorems that permeate the book are original. It also contains results and methods hitherto unknown in the constructive and nonconstructive settings. The text features logic only in the common sense and, beyond a certain mathematical maturity, requires no prior training in either constructive mathematics or probability theory. It will thus be accessible and of interest, both to probabilists interested in the foundations of their speciality and to constructive mathematicians who wish to see Bishop's theory applied to a particular field.
R 7,406.14
Examining Quantum Algorithms for Quantum Image Processing
An emerging interdisciplinary field of study in the realm of academia has been quantum computing and its various applications. The rapid rate of progress of this advancing technology as well as its multi-faceted nature has created a vast amount of potential research material for professionals and students in numerous disciplines. Its specific ability to improve upon traditional algorithms for image processing is seizing the attention of researchers in this field, as there remains a lack of exploration into this precise area. Examining Quantum Algorithms for Quantum Image Processing is an essential reference that provides research on quantum Fourier transform, quantum wavelet transform, and quantum wavelet packet transform as tool algorithms in image processing and quantum computing. It provides a comprehensive look into quantum image algorithms to establish frameworks of quantum image processing. While highlighting topics including geometric transformation, quantum compression ratio, and storage circuits, this book is ideally designed for researchers, scientists, developers, academicians, programmers, practitioners, engineers, and upper graduate students.
R 6,407.90
Bankruptcy Prediction through Soft Computing based Deep Learning Technique
This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.
R 1,527.28
R 1,205.32
Deep Learning with Applications Using Python
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.Use face recognition and face detection capabilitiesCreate speech-to-text and text-to-speech functionalityEngage with chatbots using deep learningWho This Book Is ForData scientists and developers who want to adapt and build deep learning applications.
R 1,949.89
Practical MATLAB Deep Learning
Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning.Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include:Aircraft navigationAn aircraft that lands on Titan, the moon of Saturn, using reinforcement learningStock market predictionNatural language processingMusic creation usng generative deep learningPlasma controlEarth sensor processing for spacecraftMATLAB Bluetooth data acquisition applied to dance physics What You Will LearnExplore deep learning using MATLAB and compare it to algorithmsWrite a deep learning function in MATLAB and train it with examplesUse MATLAB toolboxes related to deep learningImplement tokamak disruption predictionNow includes reinforcement learningWho This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.
R 1,368.00
Synthetic Data for Deep Learning
Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That’s where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect.Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You’ll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You’ll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications.After completing this book, you’ll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making.What You Will LearnCreate synthetic tabular data with R and PythonUnderstand how synthetic data is important for artificial neural networksMaster the benefits and challenges of synthetic dataUnderstand concepts such as domain randomization and domain adaptation related to synthetic data generationWho This Book Is ForThose who want to learn about synthetic data and its applications, especially professionals working in the field of machine learning and computer vision. This book will also be useful for graduate and doctoral students interested in this subject.
R 1,934.58
Deep Learning for Data Analytics
Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.
R 5,002.00
Machine Learning With Go
Build simple, maintainable, and easy to deploy machine learning applications.About This Book• Build simple, but powerful, machine learning applications that leverage Go's standard library along with popular Go packages.• Learn the statistics, algorithms, and techniques needed to successfully implement machine learning in Go• Understand when and how to integrate certain types of machine learning model in Go applications.Who This Book Is ForThis book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary.What You Will Learn• Learn about data gathering, organization, parsing, and cleaning.• Explore matrices, linear algebra, statistics, and probability.• See how to evaluate and validate models.• Look at regression, classification, clustering.• Learn about neural networks and deep learning• Utilize times series models and anomaly detection.• Get to grip with techniques for deploying and distributing analyses and models.• Optimize machine learning workflow techniquesIn DetailThe mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.Style and approachThis book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.
R 1,889.00
Advancements in Machine Learning and Natural Language Processing: Innovations and Applications
This book discovers groundbreaking advancements in artificial intelligence with innovative solutions for real-world challenges. This book showcases state-of-the-art methodologies like deep learning and transfer learning to tackle sentiment analysis, fake news detection, and multi-dialectal named entity recognition, with a special focus on Arabic language technologies. Bridging the gap between research and practice, it highlights topics such as knowledge extraction, AI ethics, and the societal impacts of big data. Targeted at researchers, educators, and professionals, it serves as a vital guide for beginners and a comprehensive reference for experts seeking to stay ahead in the rapidly evolving field of AI.
R 9,495.18
Deep Learning for 3D Point Clouds
As an efficient 3D vision solution, point clouds have been widely applied into diverse engineering scenarios, including immersive media communication, autonomous driving, reverse engineering, robots, topography mapping, digital twin city, medical analysis, digital museum, etc. Thanks to the great developments of deep learning theories and methods, 3D point cloud technologies have undergone fast growth during the past few years, including diverse processing and understanding tasks. Human and machine perception can be benefited from the success of using deep learning approaches, which can significantly improve 3D perception modeling and optimization, as well as 3D pre-trained and large models. This book delves into these research frontiers of deep learning-based point cloud technologies.The subject of this book focuses on diverse intelligent processing technologies for the fast-growing 3D point cloud applications, especially using deep learning-based approaches. The deep learning-based enhancement and analysis methods are elaborated in detail, as well as the pre-trained and large models with 3D point clouds. This book carefully presents and discusses the newest progresses in the field of deep learning-based point cloud technologies, including basic concepts, fundamental background knowledge, enhancement, analysis, 3D pre-trained and large models, multi-modal learning, open source projects, engineering applications, and future prospects.Readers can systematically learn the knowledge and the latest developments in the field of deep learning-based point cloud technologies. This book provides vivid illustrations and examples, and the intelligent processing methods for 3D point clouds. Readers can be equipped with an in-depth understanding of the latest advancements of this rapidly developing research field.
R 8,433.60
Course in Computational Probability and Statistics
This book arose out of a number of different contexts, and numerous persons have contributed to its conception and development. It had its origin in a project initiated jointly with the IBM Cambridge Scien tific Center, particularly with Dr. Rhett Tsao, then of that Center. We are grateful to Mr. Norman Rasmussen, Manager of the IBM Scientific Center Complex, for his initial support. The work is being carried on at Brown University with generous support from the Office of Computing Activities of the National Science Foundation (grants GJ-174 and GJ-7l0); we are grateful to Dr. John Lehmann of this Office for his interest and encouragement. Professors Donald McClure and Richard Vitale of the Division of Applied Mathematics at Brown University contributed greatly to the project and taught courses in its spirit. We are indebted to them and to Dr. Tore Dalenius of the University of Stockholm for helpful criticisms of the manuscript. The final stimulus to the book's completion came from an invLtation to teach a course at the IBM European Systems Research Institute at Geneva. We are grateful to Dr. J.F. Blackburn, Director of the Institute, for his invitation, and to him and his wife Beverley for their hospitality. We are greatly indebted to Mrs. Katrina Avery for her splendid secretarial and editorial work on the manuscript.
R 1,540.30
Practical Deep Learning for Cloud and Mobile
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly. Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi Learn by building examples such as Silicon Valley's "Not Hotdog," image search engines, and your own mini-autonomous car Use transfer learning to train models in minutes Optimize your apps to run efficiently on different hardware Discover strategies to scale up from a single user to millions Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology
R 2,052.00
Embedded Deep Learning
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes;Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
R 4,038.24
Fog Computing, Deep Learning and Big Data Analytics-Research Directions
This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.
R 4,619.32
Deep Learning Enabled Semantic Communications
Comprehensive overview of the principles, theories, and techniques behind deep learning-enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning-enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about the deep learning and semantic communications, including the history, neural networks, and semantic information theoryCompression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speechIncorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiverIntroduction of semantic impairments in communications to uncover how to design robust systemsJoint design of data sampling, compression, and coding schemes under the guidance of semantic informationFramework of generative semantic communications to detail the principles of incorporating generative models into semantic communications Deep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.
R 4,559.00
Deep Learning Illustrated
"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come."—Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep LearningDeep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.World-class instructor and practitioner Jon Krohn—with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens—presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered.You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitionersExplore new tools that make deep learning models easier to build, use, and improveMaster essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and moreWalk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
R 1,838.00
R 1,222.00
Computer Vision – ECCV 2024
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29-October 4, 2024. The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation.
R 3,173.85
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
Probability with Martingales
Probability theory is nowadays applied in a huge variety of fields including physics, engineering, biology, economics and the social sciences. This book is a modern, lively and rigorous account which has Doob's theory of martingales in discrete time as its main theme. It proves important results such as Kolmogorov's Strong Law of Large Numbers and the Three-Series Theorem by martingale techniques, and the Central Limit Theorem via the use of characteristic functions. A distinguishing feature is its determination to keep the probability flowing at a nice tempo. It achieves this by being selective rather than encyclopaedic, presenting only what is essential to understand the fundamentals; and it assumes certain key results from measure theory in the main text. These measure-theoretic results are proved in full in appendices, so that the book is completely self-contained. The book is written for students, not for researchers, and has evolved through several years of class testing. Exercises play a vital rôle. Interesting and challenging problems, some with hints, consolidate what has already been learnt, and provide motivation to discover more of the subject than can be covered in a single introduction.
R 2,153.33
Typicality Reasoning in Probability, Physics, and Metaphysics
This book provides a comprehensive investigation into the concept of typicality and its significance for physics and the philosophy of science. It identifies typicality as a fundamental way of reasoning, central to how natural laws explain and are tested against phenomena. The book discusses various applications of typicality to foundational questions in physics and beyond.These include:a unified interpretation of objective probabilities in classical mechanics and quantum mechanicsa detailed discussion of Boltzmann's statistical mechanics, entropy, and the second law of thermodynamicsa novel account of the asymmetry of causation and the arrow of time Finally, the book turns to the question: "What are laws of nature"? It argues that typicality extends to a powerful way of reasoning in metaphysics that can and should inform our commitments about the fundamental ontology of the world. On this basis, it develops an argument against the Humean best system account, according to which laws of nature are merely an efficient summary of contingent regularities.
R 6,576.92
Deep Learning in Drug Design
Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models, etc., as well as deep learning applications in various aspects of drug design. This book offers a comprehensive academic overview of deep learning in drug design. It begins with molecular representations, CNNs, GNNs, Transformers, generative models, explainable AI, large models, etc. Next, it covers deep learning applications like protein structure prediction, molecular interactions, ADMET prediction, antibody design, and so on. Finally, a separate chapter is dedicated to the introduction of the ethics and regulation of artificial intelligence in drug design. This book is ideal for readers aiming to learn and implement deep learning methods and applications in drug design and related fields.Deep Learning in Drug Design: Methods and Applications is particularly helpful to undergraduate, graduate, and doctoral students in need of a practical guide to the principles of the discipline. Established researchers in the area will benefit from the detailed case studies and algorithms presented.
R 4,414.00
Deep Learning Techniques for IoT Security and Privacy
This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.
R 5,562.54
Deep Learning Methods For Image Object Detection And Recognition
This book provides a comprehensive introduction on applying deep learning to the visual recognition of objects, bridging the gap between theoretical algorithms and high-impact practical implementations. Within these pages, readers will find a detailed exploration of advanced techniques for image restoration—including UNet-based defogging, feature fusion GANs, and ESRGAN super-resolution—alongside high-performance detection models. Specialized applications such as underwater crack segmentation using transfer learning, marine biological detection, and real-time analysis through YOLOv4, RetinaNet, and LSTM-integrated networks are also covered to provide researchers and engineers with a technical blueprint for solving diverse real-world computer vision challenges. Additionally, this work is also suitable as a textbook for advanced undergraduate and graduate students majoring in Artificial Intelligence, Intelligent Science and Technology, Computer Science, and Automation.
R 3,590.00
Deep Learning with R
Description Artificial intelligence has made some incredible leaps. Deep learning systems now deliver near-human speech and image recognition, not to mention machines capable of beating world champion Go masters. Deep learning applies to a widening range of problems, such as question answering, machine translation, and optical character recognition. It’s behind photo tagging, self-driving cars, virtual assistants and other previously impossible applications. Deep Learning with R is for developers and data scientists with some R experience who want to use deep learning to solve real-world problems. The book is structured around a series of practical examples that introduce each new concept and demonstrate best practices. You’ll begin by learning what deep learning is, how it connects with AI and Machine Learning, and why it’s rapidly gaining in importance right now. You’ll then dive into practical applications of computer vision, natural language processing, and more. Key features · Understand key machine learning concepts · Set up a computer environment for deep learning · Visualize neural networks · Use recurrent neural networks for text and sequence Classification Audience You’ll need intermediate R programming skills. No previous experience with machine learning or deep learning is required. About the technology Although deep learning can be a challenging subject, new technologies make it much easier to get started than ever before. The Keras deep learning library featured in this book puts ease of use and accessibility front and center, making it a great fit for new practitioners.
R 1,303.00
Examining an Operational Approach to Teaching Probability
Several years ago, there began a consideration of the inadequacy of a traditional approach to teaching mathematics. Many teachers and perhaps a majority of the students often realize something is wrong with these methods and report a lack of enthusiasm in dealing with the discipline. Many teachers think that certain established habits have a serious pedagogical basis, and therefore, it is difficult to question them. In addition, perhaps, there is also a certain fear in imagining and experimenting with new ways. Unfortunately, the excessive use of examples and abstract formulations with exclusive reference to algebraic language distances the student from the pleasure of the discipline. Mathematics, on the other hand, requires attention and concentration, but the understanding of its meaning gives rise to interest, pleasure to discover, and promotes deep learning. This is where studying probability from an operational approach has gained much traction. The most interesting aspect is the use of a very artisanal approach, starting with objects that students can, in part, find in their daily lives. Trying to identify objects and situations that speak of ""different mathematics,"" embodied in everyday life, may offer more possibilities to deal with the mathematical illiteracy that seems to afflict a large part of our society. Examining an Operational Approach to Teaching Probability focuses on probability examined from an educational point of view and the implementation of a very concrete operational approach in the classroom. Two main pillars are examined within this book: concrete objects and IT tools used to perform simulations for probability teaching. Each chapter is devoted to an essential concept related to probability and covers the operational approach all the way from its historical development to types of probability studies, different teaching methods within the approach, and the theories surrounding it. This book is ideal for pre-service and in-service teachers looking for nontraditional approaches in teaching along with instructional designers, curricula developers, practitioners, researchers, academicians, and students interested in learning more about operational research and the use of objects to introduce probabilistic concepts in a new method of teaching.
R 6,421.23
Deep Learning with Keras Workshop
Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning modelsKey FeaturesGet to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scoresExplore advanced concepts such as sequential memory and sequential modelingReinforce your skills with real-world development, screencasts, and knowledge checksBook DescriptionNew experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks.What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data.By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.What you will learnGain insights into the fundamentals of neural networksUnderstand the limitations of machine learning and how it differs from deep learningBuild image classifiers with convolutional neural networksEvaluate, tweak, and improve your models with techniques such as cross-validationCreate prediction models to detect data patterns and make predictionsImprove model accuracy with L1, L2, and dropout regularizationWho this book is forIf you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.
R 1,335.00
Hands-on Deep Learning
This book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will LearnWhat are deep neural networks?What is transfer learning, multi-task learning, and end-to-end learning?What are hyperparameters, bias, variance, and data division?What are CNN and RNN? Who This Book Is ForMachine learning engineers, data scientists, AI practitioners, software developers, and engineers interested in deep learning
R 1,482.00
Multi-scale Quantum Models for Biocatalysis
“Multi-scale Quantum Models for Biocatalysis” explores various molecular modelling techniques and their applications in providing an understanding of the detailed mechanisms at play during biocatalysis in enzyme and ribozyme systems. These areas are reviewed by an international team of experts in theoretical, computational chemistry, and biophysics.This book presents detailed reviews concerning the development of various techniques, including ab initio molecular dynamics, density functional theory, combined QM/MM methods, solvation models, force field methods, and free-energy estimation techniques, as well as successful applications of multi-scale methods in the biocatalysis systems including several protein enzymes and ribozymes.This book is an excellent source of information for research professionals involved in computational chemistry and physics, material science, nanotechnology, rational drug design and molecular biology and for students exposed to these research areas.
R 9,909.75
Foundations of Computing and Machine Learning
This volume is a celebration of Leslie Valiant's scientific and intellectual achievements. It contains eight of Valiant's most impactful research works, along with accompanying contributions providing modern perspectives on these landmark papers in the context of contemporary research in complexity theory, parallel computing, computational learning theory, and machine learning. It also contains a biography of Leslie Valiant, an interview with him, and his Turing Award lecture. In 2010, Valiant received the ACM's Turing Award for "transformative contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of enumeration and of algebraic computation, and the theory of parallel and distributed computing." His research contributions have also been recognized with the International Mathematical Union's Nevanlinna Prize in 1986, the Knuth Prize in 1997, and many other awards. Leslie Valiant has made deep contributions to an unusually broad range of areas within theoretical computer science. Perhaps his most celebrated contribution has been his paper "A Theory of the Learnable," which gave birth to the field of computational learning theory and provided much of the intellectual foundation for the highly successful development of modern machine learning. His pioneering research in computational complexity inaugurated the study of the complexity of counting problems, defining the fundamental "counting complexity" class #P and showing that a wide range of natural counting problems are complete for this class. Valiant defined algebraic analogues of the complexity classes P and NP and established the first completeness results for these classes; this groundbreaking work opened new vistas for the field of algebraic complexity theory that continue to be explored to this day. Valiant also made seminal contributions to the field of parallel computing through his development of the highly impactful Bulk Synchronous Parallel (BSP) model as a "bridging" model between parallel software and parallel hardware, and he gave an unexpected randomized message-routing algorithm for parallel computers, which has been highly influential.
R 3,092.00
R 2,260.70
Angular and Deep Learning Pocket Primer
This book provides readers with enough information for them to develop more sophisticated Angular applications that incorporate deep learning. The first three chapters of this book contain a short tour of basic Angular functionality, such as UI components and forms in Angular applications. The fourth chapter introduces you to deep learning, the problems it can solve, and some challenges for the future. You will also learn about MLPs (Multi Layer Perceptrons), CNNs (Convolutional Neural Networks), and a Keras-based code sample of a CNN with the MNIST dataset. The fifth chapter discusses RNNs (Recurrent Neural Networks), BPTT (Back Propagation Through Time), as well as LSTMs (Long Short Term Memory) and AEs (Auto Encoders). The sixth chapter introduces basic TensorFlow concepts, followedby tensorflowjs (i.e., TensorFlow in modern browsers), and some examples of Angular applications combined with deep learning.
R 1,026.00
Quantum Computing For Dummies
Comprehend the mysteries—and the amazing potential—of quantum computing Quantum computing has the promise to be the next huge thing in technology. How do we know that? Look at how much the big players in tech are investing in the technology. Quantum Computing For Dummies preps you for the amazing changes that are coming with the world of computing built on the phenomena of quantum mechanics. Need to know what is it and how does it work? This easy-to-understand book breaks it down and answers your most pressing questions. Get a better understanding of how quantum computing is revolutionizing networking, data management, cryptography, and artificial intelligence in ways that would have previously been unthinkable. With a Dummies guide by your side, you’ll get a primer on the inner workings and practical applications of quantum computers. Learn the difference binary and quantum computers Discover which industries will be most influenced by quantum computingSee how quantum improves encryption and enables businessTake a look at how quantum is applied in big data and AIFor technologists and IT pros interested in getting on board the quantum train—plus anyone who’s quantum-curious—this Dummies guide is a must-have.
R 711.00
Probability and Statistics for Data Science
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.Prerequisites are calculus, some matrix algebra, and some experience in programming.Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
R 7,525.35
Deep Learning Through the Prism of Tensors
In the rapidly evolving field of artificial intelligence, this book serves as a crucial resource for understanding the mathematical foundations of AI. It explores the intricate world of tensors, the fundamental elements powering today's advanced deep learning models. Combining theoretical depth with practical insights, the text navigates the complex landscape of tensor calculus, guiding readers to master the principles and applications of tensors in AI. From the basics of tensor algebra and geometry to the sophisticated architectures of neural networks, including multi-layer perceptrons, convolutional, recurrent, and transformer models, this book provides a comprehensive examination of the mechanisms driving modern AI innovations. It delves into the specifics of autoencoders, generative models, and geometric interpretations, offering a fresh perspective on the complex, high-dimensional spaces traversed by deep learning technologies. Concluding with a forward-looking view, the book addresses the latest advancements and speculates on the future directions of AI research, preparing readers to contribute to or navigate the next wave of innovations in the field. Designed for academics, researchers, and industry professionals, it serves as both an essential textbook for graduate and postgraduate students and a valuable reference for experts in the field. With its rigorous approach to the mathematical frameworks of AI and a strong focus on practical applications, this book bridges the gap between theoretical research and real-world implementation, making it an indispensable guide in the realm of artificial intelligence.
R 4,030.00
Deep Learning with Azure
Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer.Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI?Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI.What You'll LearnBecome familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AIUse pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more)Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolvingDiscover the options for training and operationalizing deep learning models on AzureWho This Book Is ForProfessional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
R 1,693.28
Advanced Applications of NLP and Deep Learning in Social Media Data
Social media platforms are one of the main generators of textual data where people around the world share their daily life experiences and information with online society. The social, personal, and professional lives of people on these social networking sites generate not only a huge amount of data but also open doors for researchers and academicians with numerous research opportunities. This ample amount of data needs advanced machine learning, deep learning, and intelligent tools and techniques to receive, process, and interpret the information to resolve real-life challenges and improve the online social lives of people. Advanced Applications of NLP and Deep Learning in Social Media Data bridges the gap between natural language processing (NLP), advanced machine learning, deep learning, and online social media. It hopes to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. Covering topics such as machine learning-based prediction, emotion recognition, and high-dimensional text clustering, this premier reference source is an essential resource for OSN service providers, psychiatrists, psychologists, clinicians, sociologists, students and educators of higher education, librarians, researchers, and academicians.
R 7,926.65
Semantics and Deep Learning
This Element covers the interaction of two research areas: linguistic semantics and deep learning. It focuses on three phenomena central to natural language interpretation: reasoning and inference; compositionality; extralinguistic grounding. Representation of these phenomena in recent neural models is discussed, along with the quality of these representations and ways to evaluate them (datasets, tests, measures). The Element closes with suggestions on possible deeper interactions between theoretical semantics and language technology based on deep learning models.
R 930.20
Research on Teaching and Learning Probability
This book summarizes the vast amount of research related to teaching and learning probability that has been conducted for more than 50 years in a variety of disciplines. It begins with a synthesis of the most important probability interpretations throughout history: intuitive, classical, frequentist, subjective, logical propensity and axiomatic views. It discusses their possible applications, philosophical problems, as well as their potential and the level of interest they enjoy at different educational levels. Next, the book describes the main features of probabilistic thinking and reasoning, including the contrast to classical logic, probability language features, the role of intuitions, as well as paradoxes and the relevance of modeling. It presents an analysis of the differences between conditioning and causation, the variability expression in data as a sum of random and causal variations, as well as those of probabilistic versus statistical thinking. This is followed by an analysis of probability’s role and main presence in school curricula and an outline of the central expectations in recent curricular guidelines at the primary, secondary and high school level in several countries. This book classifies and discusses in detail the three different research periods on students’ and people’s intuitions and difficulties concerning probability: early research focused on cognitive development, a period of heuristics and biases programs, and the current period marked by a multitude of foci, approaches and theoretical frameworks.
R 797.58
Subjective Probability
This book offers a concise survey of basic probability theory from a thoroughly subjective point of view whereby probability is a mode of judgment. Written by one of the greatest figures in the field of probability theory, the book is both a summation and synthesis of a lifetime of wrestling with these problems and issues. After an introduction to basic probability theory, there are chapters on scientific hypothesis-testing, on changing your mind in response to generally uncertain observations, on expectations of the values of random variables, on de Finetti's dissolution of the so-called problem of induction, and on decision theory.
R 3,699.36
Geometry of Deep Learning
The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems.Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
R 2,974.09
Quantile Modeling for Political Research
Quantile models are widely used across the natural and social sciences to analyze heterogeneous phenomena that mean-based methods often obscure. Yet their adoption in political science remains limited, in part because accessible, discipline-specific introductions and applications are scarce. This Element addresses the gap by offering an applied introduction to quantile models and showing how they can be incorporated into the empirical toolkit of political scientists. Combining methodological innovation with practical guidance, this Element introduces quantile models for both continuous and discrete response variables, which are illustrated with real-world political examples concerning, among others, governments, parties, voters, and legislative choices. It is intended for both students and researchers who wish to apply quantile models to obtain a richer understanding of political phenomena. This title is also available as Open Access on Cambridge Core.
R 2,978.00
