Search results for "Probability for Deep Learning Quantum"
Items we need to order for you
Deep Learning with Python, Third Edition
The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.In Deep Learning with Python, Third Edition you'll discover: Deep learning from first principlesThe latest features of Keras 3A primer on JAX, PyTorch, and TensorFlowImage classification and image segmentationTime series forecastingLarge Language modelsText classification and machine translationText and image generation—build your own GPT and diffusion models!Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images.
R 2,605.00
Probability in Banach Spaces 7
The first international conference on Probability in Banach Spaces was held at Oberwolfach, West Germany, in 1975. It brought together European researchers who, under the inspiration of the Schwartz Seminar in Paris, were using probabi listic methods in the study of the geometry of Banach spaces, a rather small number of probabilists who were already studying classical limit laws on Banach spaces, and a larger number of probabilists, specialists in various aspects of the study of Gaussian processes, whose results and techniques were of interest to the members of the first two groups. This first conference was very fruitful. It fos tered a continuing relationship among 50 to 75 probabilists and analysts working on probability on infinite-dimensional spaces, the geometry of Banach spaces, and the use of random methods in harmonic analysis. Six more international conferences were held since the 1975 meeting. Two of the meetings were held at Tufts University, one at S¢nderborg, Denmark, and the others at Oberwolfach. This volume contains a selection of papers by the partici pants of the Seventh International Conference held at Oberwolfach, West Ger many, June 26-July 2, 1988. This exciting and provocative conference was at tended by more than 50 mathematicians from many countries. These papers demonstrate the range of interests of the conference participants. In addition to the ongoing study of classical and modern limit theorems in Banach spaces, a branching out has occurred among the members of this group.
R 1,559.44
R 1,090.48
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
R 2,763.70
Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide
Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive ToolkitKey FeaturesUnderstand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to productionEvaluate the performance of your models and improve your deep learning skillsBook DescriptionCognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks.This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environmentWhat you will learnSet up your deep learning environment for the Cognitive Toolkit on Windows and LinuxPre-process and feed your data into neural networksUse neural networks to make effcient predictions and recommendationsTrain and deploy effcient neural networks such as CNN and RNNDetect problems in your neural network using TensorBoardIntegrate Cognitive Toolkit with Azure ML Services for effective deep learningWho this book is forData Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.
R 1,140.00
Deep Learning kompakt für Dummies
Wollen Sie sich über Deep Learning informieren und vielleicht erste kleine Schritte machen, diese Technologie zu nutzen? Dann hilft Ihnen dieses Buch. Die Autoren erklären Ihnen, welchen Einfluss Deep Learning im Alltag hat und in welcher Beziehung es zu maschinellem Lernen steht. Sie sammeln erste eigene Erfahrungen mit vorgegebenen Python-Programmen und verstehen so die Funktionsweise von neuronalen Netzen und wie Bilder klassifiziert und Sprache sowie Text verarbeitet werden. So ist dieses Buch ein schneller erster und verständlicher Einstieg in Deep Learning.
R 683.00
Introduction to Probability Theory With Engineering Applications
Introduction to Probability Theory with Engineering Applications provides students with a solid foundation in probability theory, which deals with the modeling of uncertainty, and illuminates several modern applications of probability in engineering, physics and data analysis.The text is organized into five chapters and three appendices. The opening chapter introduces the notion of probability as a model or representation for the uncertainty associated with statistical experiments. In additional chapters, students learn about random variables through explanations of discrete and continuous variables, conditional distribution, and statistical distribution. Students examine functions of one random variable, two random variables, and extensions to multivariable distributions. The final chapter covers random processes.Helpful appendices include six computer laboratories that correspond with the content in Chapters 2-5, assessment and review questions for each chapter, and basic results from linear algebra.The book is an ideal resource for courses in engineering, computer science, biomedicine, physics, and mathematics. It is also an excellent text for researchers seeking an overview in applied probability theory. It is assumed readers have a background in introductory calculus and computer programming.
R 5,901.14
Reinforcement Learning for Finance
This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN – two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, and loss functions.After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library.What You Will LearnUnderstand the fundamentals of reinforcement learningApply reinforcement learning programming techniques to solve quantitative-finance problemsGain insight into convolutional neural networks and recurrent neural networksUnderstand the Markov decision processWho This Book Is ForData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.
R 866.00
Quantum Mechanics: For Electrical Engineers
The book is designed for a one-semester graduate course in quantum mechanics for electrical engineers. It can also be used for teaching quantum mechanics to graduate students in materials science and engineering departments as well as to applied physicists. The selection of topics in the book is based on their relevance to engineering applications. The book provides the theoretical foundation for graduate courses in quantum optics and lasers, semiconductor electronics, applied superconductivity and quantum computing. It covers (along with traditional subjects) the following topics: resonant and Josephson tunneling; Landau levels and their relation to the integer quantum Hall effect; effective mass Schrodinger equation and semi-classical transport; quantum transitions in two-level systems; Berry phase and Berry curvature; density matrix and optical Bloch equation for two-level systems; Wigner function and quantum transport; exchange interaction and spintronic.
R 2,213.96
Law and Policy for the Quantum Age
It is often said that quantum technologies are poised to change the world as we know it, but cutting through the hype, what will quantum technologies actually mean for countries and their citizens? In Law and Policy for the Quantum Age, Chris Jay Hoofnagle and Simson L. Garfinkel explain the genesis of quantum information science (QIS) and the resulting quantum technologies that are most exciting: quantum sensing, computing, and communication. This groundbreaking, timely text explains how quantum technologies work, how countries will likely employ QIS for future national defense and what the legal landscapes will be for these nations, and how companies might (or might not) profit from the technology. Hoofnagle and Garfinkel argue that the consequences of QIS are so profound that we must begin planning for them today. This title is available as Open Access on Cambridge Core.
R 3,460.00
Probability on Discrete Structures
Most probability problems involve random variables indexed by space and/or time. These problems almost always have a version in which space and/or time are taken to be discrete. This volume deals with areas in which the discrete version is more natural than the continuous one, perhaps even the only one than can be formulated without complicated constructions and machinery. The 5 papers of this volume discuss problems in which there has been significant progress in the last few years; they are motivated by, or have been developed in parallel with, statistical physics. They include questions about asymptotic shape for stochastic growth models and for random clusters; existence, location and properties of phase transitions; speed of convergence to equilibrium in Markov chains, and in particular for Markov chains based on models with a phase transition; cut-off phenomena for random walks. The articles can be read independently of each other. Their unifying theme is that of models built on discrete spaces or graphs. Such models are often easy to formulate. Correspondingly, the book requires comparatively little previous knowledge of the machinery of probability.
R 3,048.35
Basic Probability Theory with Applications
The main intended audience for this book is undergraduate students in pure and applied sciences, especially those in engineering. Chapters 2 to 4 cover the probability theory they generally need in their training. Although the treatment of the subject is surely su?cient for non-mathematicians, I intentionally avoided getting too much into detail. For instance, topics such as mixed type random variables and the Dirac delta function are only brie?y mentioned. Courses on probability theory are often considered di?cult. However, after having taught this subject for many years, I have come to the conclusion that one of the biggest problems that the students face when they try to learn probability theory, particularly nowadays, is their de?ciencies in basic di?erential and integral calculus. Integration by parts, for example, is often already forgotten by the students when they take a course on probability. For this reason, I have decided to write a chapter reviewing the basic elements of di?erential calculus. Even though this chapter might not be covered in class, the students can refer to it when needed. In this chapter, an e?ort was made to give the readers a good idea of the use in probability theory of the concepts they should already know. Chapter 2 presents the main results of what is known as elementary probability, including Bayes’ rule and elements of combinatorial analysis.
R 1,700.48
Deep Learning for Medical Decision Support Systems
This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.
R 3,663.00
Deep Learning for Smart Healthcare
Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.
R 3,592.55
Mathematics and Programming for Machine Learning with R
Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R. The book begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to the coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with machine learning based on artificial neural networks. The first half of the book does not require mathematical sophistication, although familiarity with probability and statistics would be helpful. The second half assumes the reader is familiar with at least one semester of calculus. The text guides novice R programmers through algorithms and their application and along the way; the reader gains programming confidence in tackling advanced R programming challenges. Highlights of the book include: More than 400 exercises A strong emphasis on improving programming skills and guiding beginners to the implementation of full-fledged algorithms Coverage of fundamental computer and mathematical concepts including logic, sets, and probability In-depth explanations of machine learning algorithms
R 6,741.61
Hyperparameter Tuning for Machine and Deep Learning with R
This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.
R 2,198.00
Problems and Snapshots from the World of Probability
We, the authors of this book, are three ardent devotees of chance, or some what more precisely, of discrete probability. When we were collecting the material, we felt that one special pleasure of the field lay in its evocation of an earlier age: many of our 'probabilistic forefathers' were dexterous solvers of discrete problems. We hope that this pleasure will be transmitted to the readers. The first problem-book of a similar kind as ours is perhaps Mosteller's well-known Fifty Challenging Problems in Probability (1965). Possibly, our book is the second. The book contains 125 problems and snapshots from the world of prob ability. A 'problem' generally leads to a question with a definite answer. A 'snapshot' is either a picture or a bird's-eye view of some probabilistic field. The selection is, of course, highly subjective, and we have not even tried to cover all parts of the subject systematically. Limit theorems appear only seldom, for otherwise the book would have become unduly large. We want to state emphatically that we have not written a textbook in probability, but rather a book for browsing through when occupying an easy-chair. Therefore, ideas and results are often put forth without a machinery of formulas and derivations; the conscientious readers, who want to penetrate the whole clockwork, will soon have to move to their desks and utilize appropriate tools.
R 1,549.02
Quantum Dots for Quantum Information Technologies
This book highlights the most recent developments in quantum dot spin physics and the generation of deterministic superior non-classical light states with quantum dots. In particular, it addresses single quantum dot spin manipulation, spin-photon entanglement and the generation of single-photon and entangled photon pair states with nearly ideal properties. The role of semiconductor microcavities, nanophotonic interfaces as well as quantum photonic integrated circuits is emphasized. The latest theoretical and experimental studies of phonon-dressed light matter interaction, single-dot lasing and resonance fluorescence in QD cavity systems are also provided. The book is written by the leading experts in the field.
R 5,396.70
Theory of Probability
This book is the sixth edition of a classic text that was first published in 1950 in the former Soviet Union. The clear presentation of the subject and extensive applications supported with real data helped establish the book as a standard for the field. To date, it has been published into more that ten languages and has gone through five editions. The sixth edition is a major revision over the fifth. It contains new material and results on the Local Limit Theorem, the Integral Law of Large Numbers, and Characteristic Functions. The new edition retains the feature of developing the subject from intuitive concepts and demonstrating techniques and theory through large numbers of examples. The author has, for the first time, included a brief history of probability and its development. Exercise problems and examples have been revised and new ones added.
R 2,915.42
PyTorch 1.x Reinforcement Learning Cookbook
Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipesKey FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook DescriptionReinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use.With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game.By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is forMachine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.
R 1,498.00
Practical Deep Learning at Scale with MLflow
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey FeaturesFocus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityBook DescriptionThe book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.What you will learnUnderstand MLOps and deep learning life cycle developmentTrack deep learning models, code, data, parameters, and metricsBuild, deploy, and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is forThis book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
R 1,596.00
Deep Learning in Visual Computing
Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing.This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.
R 3,407.16
Deep Learning with PyTorch Workshop
Get a head start in the world of AI and deep learning by developing your skills with PyTorchKey FeaturesLearn how to define your own network architecture in deep learningImplement helpful methods to create and train a model using PyTorch syntaxDiscover how intelligent applications using features like image recognition and speech recognition really process your dataBook DescriptionWant to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch.It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures.The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.What you will learnExplore the different applications of deep learningUnderstand the PyTorch approach to building neural networksCreate and train your very own perceptron using PyTorchSolve regression problems using artificial neural networks (ANNs)Handle computer vision problems with convolutional neural networks (CNNs)Perform language translation tasks using recurrent neural networks (RNNs)Who this book is forThis deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.
R 1,335.00
Creating Learning Systems for Higher Education
Creating Learning Systems for Higher Education: A Quantum Field Framework presents a groundbreaking approach to education that integrates quantum physics, competence, differentiated thinking, and individual performance proficiencies into a multidimensional, dynamic, and self-organising learning field. This innovative work is designed to prepare learners to know, perform, and understand personal, professional, and social competence.Drawing on sciences, learning models, and human technologies, Moye's ‘Psychophysical Learning System’ propels learners toward success by bringing the competences required for specific disciplines into a coherent framework. This system is not discipline-specific, making it adaptable across various fields of study, making it a must-read for educators, curriculum designers, and anyone interested in the future of learning.Explore the interconnectedness and interdependencies within the knowledge, performance, and integration of information that generate competence. Delve into the concept of Proprioception of Thought, which emphasises the importance of competent thinking in driving competent actions. Understand the contingencies of performing within a discipline through Human Performance Technologies, and discover how Quantum Design Models can create a resonance between the learner and natural learning events.
R 4,504.46
Advanced Methods and Deep Learning in Computer Vision
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.
R 3,678.00
Introduction to Geometric Probability
The purpose of this book is to present the three basic ideas of geometrical probability, also known as integral geometry, in their natural framework. In this way, the relationship between the subject and enumerative combinatorics is more transparent, and the analogies can be more productively understood. The first of the three ideas is invariant measures on polyconvex sets. The authors then prove the fundamental lemma of integral geometry, namely the kinematic formula. Finally the analogues between invariant measures and finite partially ordered sets are investigated, yielding insights into Hecke algebras, Schubert varieties and the quantum world, as viewed by mathematicians. Geometers and combinatorialists will find this a most stimulating and fruitful story.
R 4,318.79
INTRODUCTION TO SCIENTIFIC MACHINE LEARNING FOR ENGINEERING STUDENTS
Introduction to Scientific Machine Learning for Engineering Students offers a first-principles introduction to scientific machine learning for advanced undergraduates, graduate students, and practicing engineers. Using probability theory as the language of uncertainty, it develops a unified framework for modeling, inference, and prediction in engineering systems.The book moves from probability, Monte Carlo methods, model calibration, and Bayesian inference to supervised learning, unsupervised learning, state-space models, Kalman filtering, probabilistic programming, and automated Bayesian inference. Physics-informed machine learning is integrated throughout, emphasizing the combination of data and physical insight.Implemented entirely in reproducible Jupyter notebooks using Python, the text helps readers understand the foundations of modern methods, build models that reflect physical and causal structure, and calibrate them rigorously with data.
R 6,155.00
Probability and Phase Transition
This volume describes the current state of knowledge of random spatial processes, particularly those arising in physics. The emphasis is on survey articles which describe areas of current interest to probabilists and physicists working on the probability theory of phase transition. Special attention is given to topics deserving further research. The principal contributions by leading researchers concern the mathematical theory of random walk, interacting particle systems, percolation, Ising and Potts models, spin glasses, cellular automata, quantum spin systems, and metastability. The level of presentation and review is particularly suitable for postgraduate and postdoctoral workers in mathematics and physics, and for advanced specialists in the probability theory of spatial disorder and phase transition.
R 4,660.77
Advanced Portfolio Optimization
This book is an innovative and comprehensive guide that provides readers with the knowledge about the latest trends, models and algorithms used to build investment portfolios and the practical skills necessary to apply them in their own investment strategies. It integrates latest advanced quantitative techniques into portfolio optimization, raises questions about which alternatives to modern portfolio theory exists and how they can be applied to improve the performance of multi-asset portfolios. It provides answers and solutions by offering practical tools and code samples that enable readers to implement advanced portfolio optimization techniques and make informed investment decisions.Portfolio Optimization goes beyond traditional portfolio theory (Quadratic Programming), incorporating last advances in convex optimization techniques and cutting-edge machine learning algorithms. It extensively addresses risk management and uncertainty quantification, teaching readers how to measure and minimize various forms of risk in their portfolios. This book goes beyond traditional back testing methodologies based on historical data for investment portfolios, incorporating tools to create synthetic datasets and robust methodologies to identify better investment strategies considering real aspects like transaction costs.The author provides several methodologies for estimating the input parameters of investment portfolio optimization models, from classical statistics to more advanced models, such as graph-based estimators and Bayesian estimators, provide a deep understanding of advanced convex optimization models and machine learning algorithms for building investment portfolios and the necessary tools to design the back testing of investment portfolios using several methodologies based on historical and synthetic datasets that allow readers identify the better investment strategies.
R 4,396.00
Recent Developments in Applied Probability and Statistics
This book is devoted to Professor Jürgen Lehn, who passed away on September 29, 2008, at the age of 67. It contains invited papers that were presented at the Wo- shop on Recent Developments in Applied Probability and Statistics Dedicated to the Memory of Professor Jürgen Lehn, Middle East Technical University (METU), Ankara, April 23–24, 2009, which was jointly organized by the Technische Univ- sität Darmstadt (TUD) and METU. The papers present surveys on recent devel- ments in the area of applied probability and statistics. In addition, papers from the Panel Discussion: Impact of Mathematics in Science, Technology and Economics are included. Jürgen Lehn was born on the 28th of April, 1941 in Karlsruhe. From 1961 to 1968 he studied mathematics in Freiburg and Karlsruhe, and obtained a Diploma in Mathematics from the University of Karlsruhe in 1968. He obtained his Ph.D. at the University of Regensburg in 1972, and his Habilitation at the University of Karlsruhe in 1978. Later in 1978, he became a C3 level professor of Mathematical Statistics at the University of Marburg. In 1980 he was promoted to a C4 level professorship in mathematics at the TUD where he was a researcher until his death.
R 1,547.99
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 2,441.00
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 1,593.81
Probability: A Lively Introduction
Probability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard texts, such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds.
R 1,695.13
Machine Learning-Driven Rational Design in Nanomedicine
This book explores how machine learning is transforming nanomedicine, with a focus on the rational design of lipid nanoparticles (LNPs) for mRNA-based therapies. Moving beyond traditional, labor-intensive workflows, it highlights AI-driven methods--such as supervised learning, data augmentation, and deep learning--for predictive modeling and in silico screening. Key topics include chemoinformatics, molecular fingerprinting, and strategies to optimize LNP transfection efficiency and biocompatibility. Real-world applications, including mRNA vaccines and personalized nanomedicines, illustrate the convergence of computational biology and pharmaceutical engineering. It also addresses the ethical considerations and regulatory challenges surrounding AI-driven drug development. This book is intended for researchers, pharmaceutical scientists, computational biologists, and professionals in the biotechnology industry who seek to leverage AI-driven methodologies in nanomedicine development.
R 2,015.00
Universal Theory For Strong Limit Theorems Of Probability
This is the first book which the universal approach to strong laws of probability is discussed in. The universal theories are described for three important objects of probability theory: sums of independent random variables, processes with independent increments and renewal processes. Further generalizations are mentioned. Besides strong laws, large deviations are of independent interest. The case of infinite variations is considered as well. Readers can examine appropriate techniques and methods. Optimality of conditions is discussed.
R 3,111.82
Collection of problems in probability theory
The Russian version of A collection of problems in probability theory contains a chapter devoted to statistics. That chapter has been omitted in this translation because, in the opinion of the editor, its content deviates somewhat from that which is suggested by the title: problems in pro bability theory. The original Russian version contains some errors; an attempt was made to correct all errors found, but perhaps a few stiII remain. An index has been added for the convenience of the reader who may be searching for a definition, a classical problem, or whatever. The index lists pages as well as problems where the indexed words appear. The book has been translated and edited with the hope of leaving as much "Russian flavor" in the text and problems as possible. Any pecu liarities present are most likely a result of this intention. August, 1972 Bryan A. Haworth viii Foreword to the Russian edition This Collection of problems in probability theory is primarily intended for university students in physics and mathematics departments. Its goal is to help the student of probability theory to master the theory more pro foundly and to acquaint him with the application of probability theory methods to the solution of practical problems. This collection is geared basically to the third edition of the GNEDENKO textbook Course in proba bility theory, Fizmatgiz, Moscow (1961), Probability theory, Chelsea (1965).
R 1,533.60
R 4,531.00
Probability Path
Many probability books are written by mathematicians and have the built-in bias that the reader is assumed to be a mathematician coming to the material for its beauty. This textbook is geared towards beginning graduate students from a variety of disciplines whose primary focus is not necessarily mathematics for its own sake. Instead, A Probability Path is designed for those requiring a deep understanding of advanced probability for their research in statistics, applied probability, biology, operations research, mathematical finance and engineering. A one-semester course is laid out in an efficient and readable manner covering the core material. The first three chapters provide a functioning knowledge of measure theory. Chapter 4 discusses independence, with expectation and integration covered in Chapter 5, followed by topics on different modes of convergence, laws of large numbers with applications to statistics (quantile and distribution function estimation) and applied probability. Two subsequent chapters offer a careful treatment of convergence in distribution and the central limit theorem. The final chapter treats conditional expectation and martingales, closing with a discussion of two fundamental theorems of mathematical finance. Like Adventures in Stochastic Processes, Resnick’s related and very successful textbook, A Probability Path is rich in appropriate examples, illustrations and problems and is suitable for classroom use or self-study. The present uncorrected, softcover reprint is designed to make this classic textbook available to a wider audience. This book is different from the classical textbooks on probability theory in that it treats the measure theoretic background not as a prerequisite but as an integral part of probability theory. The result is that the reader gets a thorough and well-structured framework needed to understand the deeper concepts of current day advanced probability as it is used in statistics, engineering, biology and finance.... The pace of the book is quick and disciplined. Yet there are ample examples sprinkled over the entire book and each chapter finishes with a wealthy section of inspiring problems. —Publications of the International Statistical Institute This textbook offers material for a one-semester course in probability, addressed to students whose primary focus is not necessarily mathematics.... Each chapter is completed by an exercises section. Carefully selected examples enlighten the reader in many situations. The book is an excellent introduction to probability and its applications. —Revue Roumaine de Mathématiques Pures et Appliquées
R 2,526.60
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith Chintala
R 1,824.00
Probability in Banach Spaces, 9
The papers contained in this volume are an indication of the topics th discussed and the interests of the participants of The 9 International Conference on Probability in Banach Spaces, held at Sandjberg, Denmark, August 16-21, 1993. A glance at the table of contents indicates the broad range of topics covered at this conference. What defines research in this field is not so much the topics considered but the generality of the ques tions that are asked. The goal is to examine the behavior of large classes of stochastic processes and to describe it in terms of a few simple prop erties that the processes share. The reward of research like this is that occasionally one can gain deep insight, even about familiar processes, by stripping away details, that in hindsight turn out to be extraneous. A good understanding about the disciplines involved in this field can be obtained from the recent book, Probability in Banach Spaces, Springer-Verlag, by M. Ledoux and M. Thlagrand. On page 5, of this book, there is a list of previous conferences in probability in Banach spaces, including the other eight international conferences. One can see that research in this field over the last twenty years has contributed significantly to knowledge in probability and has had important applications in many other branches of mathematics, most notably in statistics and functional analysis.
R 4,677.66
Deep Learning Models towards Health Informatics Management
This book provides a comprehensive discussion of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, deep learning methods for medical image analysis, and deep learning-based clinical computer-aided diagnosis systems. It further presents algorithms, models, software, and tools in the field of bioinformatics.This book:Presents mathematical principles of deep learning algorithms such as convolutional neural networks, and recurrent neural networks. Discusses applications of deep learning such as hyperparameter optimization and multimodal deep learning for bioinformatics.Showcases how algorithms are applied to a broad range of application areas, including microscopy and pathology. Covers deep learning techniques such as deep feedforward networks, sequence modeling, and convolutional networks.Examines the importance of deep learning in biomedical image processing and enhancing biological diagnosis. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics, and communications engineering, computer engineering, and information technology.
R 9,327.45
Keras Deep Learning Cookbook
Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learningWhat you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras library Understand the different Keras layers Use Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.
R 1,335.00
R 766.92
