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Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
1

Cost-Sensitive Machine Learning

Cost-Sensitive Machine Learning

In machine learning applications practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting validation and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

GBP 59.99
1

Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation the authors explore how machine learning techniques apply to bioinformatics problems such as electron density map interpretation biclustering DNA sequence analysis and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

GBP 59.99
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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology cellular neuroscience neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN) clustering and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
1

Hands-On Machine Learning with R

Hands-On Machine Learning with R

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R which includes using various R packages such as glmnet h2o ranger xgboost keras and others to effectively model and gain insight from their data. The book favors a hands-on approach providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book the reader will be exposed to the entire machine learning process including feature engineering resampling hyperparameter tuning model evaluation and interpretation. The reader will be exposed to powerful algorithms such as regularized regression random forests gradient boosting machines deep learning generalized low rank models and more! By favoring a hands-on approach and using real word data the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages understand when and how to tune the various hyperparameters and be able to interpret model results. By the end of this book the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering resampling deep learning and more. · Uses a hands-on approach and real world data.

GBP 82.99
1

Applied Genetic Programming and Machine Learning

Applied Genetic Programming and Machine Learning

What do financial data prediction day-trading rule development and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications. Reflecting rapidly developing concepts and emerging paradigms this book outlines how to use machine learning techniques make learning operators that efficiently sample a search space navigate the search process through the design of objective fitness functions and examine the search performance of the evolutionary system. It provides a methodology for integrating GP and machine learning techniques establishing a robust evolutionary framework for addressing tasks from areas such as chaotic time-series prediction system identification financial forecasting classification and data mining. The book provides a starting point for the research of extended GP frameworks with the integration of several machine learning schemes. Drawing on empirical studies taken from fields such as system identification finanical engineering and bio-informatics it demonstrates how the proposed methodology can be useful in practical inductive problem solving.

GBP 59.99
1

Applications of Optimization and Machine Learning in Image Processing and IoT

Applications of Optimization and Machine Learning in Image Processing and IoT

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals. The applications of machine learning and IoT are explored with topics including optimization algorithms and machine learning in image processing and IoT. Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss the fields of machine learning IoT and image processing. KEY FEATURES: • Includes fundamental concepts towards advanced applications in machine learning and IoT. • Discusses potential and challenges of machine learning for IoT and optimization • Reviews recent advancements in diverse researches on computer vision networking and optimization field. • Presents latest technologies such as machine learning in image processing and IoT This book has been written for readers in academia engineering IT specialists researchers industrial professionals and students and is a great reference for those just starting out in the field as well as those at an advanced level.

GBP 48.99
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Physics of Data Science and Machine Learning

Physics of Data Science and Machine Learning

Physics of Data Science and Machine Learning links fundamental concepts of physics to data science machine learning and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists marrying quantum and statistical mechanics with modern data mining data science and machine learning. It also explains how to integrate these techniques into the design of experiments while exploring neural networks and machine learning building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians alongside graduate students looking to understand the basic concepts and foundations of data science machine learning and artificial intelligence. Although specifically written for physicists it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand adopt and adapt. Free from endless derivations; instead equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies York University Toronto Canada. He is also an associate researcher at Ryerson University Toronto Canada and president of the Eminent-Tech Corporation Bradford ON Canada.

GBP 56.99
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Statistical Reinforcement Learning Modern Machine Learning Approaches

Statistical Reinforcement Learning Modern Machine Learning Approaches

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence plant control and gaming the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches including model-based and model-free approaches policy iteration and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspectiveLays out the associated optimization problems for each reinforcement learning scenario coveredProvides thought-provoking statistical treatment of reinforcement learning algorithmsThe book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs as well as researchers and engineers in related fields. | Statistical Reinforcement Learning Modern Machine Learning Approaches

GBP 44.99
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Machine Learning Animated

Machine Learning Animated

The release of ChatGPT has kicked off an arms race in Machine Learning (ML) however ML has also been described as a black box and very hard to understand. Machine Learning Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning witnessing AI achieve super-human performance in Atari games such as Breakout Space Invaders Seaquest and Beam Rider. Written in a clear and concise style illustrated with animations and images this book is particularly appealing to readers with no background in computer science mathematics or statistics. Access the book's repository at: https://github. com/markhliu/MLA

GBP 74.99
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Machine Learning in Healthcare Fundamentals and Recent Applications

Machine Learning in Healthcare Fundamentals and Recent Applications

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services real estate business consumer goods social media etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology variability in medical data small datasets and lack of standard guidelines for application of AI. Therefore the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis early detection and diagnosis of disease providing objective-based evidence to reduce human errors curtailing inter- and intra-observer errors risk identification and interventions for healthcare management real-time health monitoring assisting clinicians and patients for selecting appropriate medications and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems. | Machine Learning in Healthcare Fundamentals and Recent Applications

GBP 82.99
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Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making

Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection data cleaning and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets - choosing an appropriate machine learning model results in accurate analyzing forecasting the future and making informed decisions. The global machine learning market was valued at $1. 58 billion in 2017 and is expected to reach $20. 83 billion in 2024 - growing at a CAGR of 44. 06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies. Essentially this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners industrialists technologists and researchers. | Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making

GBP 48.99
1

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences

From the Foreword:While large-scale machine learning and data mining have greatly impacted a range of commercial applications their use in the field of Earth sciences is still in the early stages. This book edited by AshokSrivastava Ramakrishna Nemani and Karsten Steinhaeuser serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences. Vipin Kumar University of MinnesotaLarge-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science computer science statistics and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources in the final chapter of the book.

GBP 44.99
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Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

Machine Learning a branch of Artificial Intelligence is influencing the society industry and academia at large. The adaptability of Python programming language to Machine Learning has increased its popularity further. Another technology on the horizon is Internet of Things (IoT). The present book tries to address IoT Python and Machine Learning along with a small introduction to Image Processing. If you are a novice programmer or have just started exploring IoT or Machine Learning with Python then this book is for you. Features: Raspberry Pi as IoT is described along with the procedure for installation and configuration. A simple introduction to Python Programming Language along with its popular library packages like NumPy Pandas SciPy and Matplotlib are dealt in an exhaustive manner along with relevant examples. Machine Learning along with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT is introduced to the audience who love to apply Machine Learning algorithms to Images The book follows hands-on approach and provide a huge collection of Python programs. | Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi

GBP 44.99
1

The Handbook of Human-Machine Interaction A Human-Centered Design Approach

The Handbook of Human-Machine Interaction A Human-Centered Design Approach

The Handbook of Human-Machine Interaction features 20 original chapters and a conclusion focusing on human-machine interaction (HMI) from analysis design and evaluation perspectives. It offers a comprehensive range of principles methods techniques and tools to provide the reader with a clear knowledge of the current academic and industry practice and debate that define the field. The text considers physical cognitive social and emotional aspects and is illustrated by key application domains such as aerospace automotive medicine and defence. Above all this volume is designed as a research guide that will both inform readers on the basics of human-machine interaction from academic and industrial perspectives and also provide a view ahead at the means through which human-centered designers including engineers and human factors specialists will attempt to design and develop human-machine systems. | The Handbook of Human-Machine Interaction A Human-Centered Design Approach

GBP 52.99
1

Text Mining with Machine Learning Principles and Techniques

Text Mining with Machine Learning Principles and Techniques

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets conclusions which are not normally evident emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject e. g. e-mail service providers online shoppers librarians etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning presenting various algorithms with their use and possibilities and reviews the positives and negatives. Beginning with the initial data pre-processing a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results the book also provides explanations of the algorithms which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources. | Text Mining with Machine Learning Principles and Techniques

GBP 44.99
1

Machine Learning for Social and Behavioral Research

Machine Learning for Social and Behavioral Research

Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large complex data sets using machine learning tools and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory early childhood learning and more) with a focus on the interplay of statistical algorithm data and theory. The identification of heterogeneity measurement error regularization and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis structural equation models and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples. | Machine Learning for Social and Behavioral Research

GBP 51.99
1

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine learning is an exciting topic with a myriad of applications. However most textbooks are targeted towards computer science students. This however creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering targeting senior undergraduate and graduate students in physics and electrical engineering alongside advanced researchers. All codes are available on the author's website: C•Lab (nau. edu) They are also available on GitHub: https://github. com/StxGuy/MachineLearning Key Features: Includes detailed algorithms. Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences. All algorithms are presented with a good mathematical background. | Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

GBP 64.99
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Machine Learning Theory to Applications

Machine Learning Theory to Applications

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars cognitive decision making communication networks security and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms covers state-of-the-art practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications. | Machine Learning Theory to Applications

GBP 52.99
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Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
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Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
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Deep and Shallow Machine Learning in Music and Audio

Deep and Shallow Machine Learning in Music and Audio

Providing an essential and unique bridge between the theories of signal processing machine learning and artificial intelligence (AI) in music this book provides a holistic overview of foundational ideas in music from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place this book explores how sound may be represented and manipulated by computer systems and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory without the frustrations of free-form coding. Surveying state-of-the art methods in applications of deep neural networks to audio and sound computing as well as offering a research perspective that suggests future challenges in music and AI research this book appeals to both students of AI and music as well as industry professionals in the fields of machine learning music and AI. | Deep and Shallow Machine Learning in Music and Audio

GBP 44.99
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Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing

Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing

The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. Advances in Complex Decision Making: Using Machine Learning and Tools for Service-Oriented Computing is a cutting-edge technical guide exploring the latest decision-making technology advancements. This book provides a comprehensive overview of machine learning algorithms and examines their applications in complex decision-making systems in a service-oriented framework. The authors also delve into service-oriented computing and how it can be used to build complex systems that support decision making. Many real-world examples are discussed in this book to provide a practical insight into how discussed techniques can be applied in various domains including distributed computing cloud computing IoT and other online platforms. For researchers students data scientists and technical practitioners this book offers a deep dive into the current developments of machine learning algorithms and their applications in service-oriented computing. This book discusses various topics including Fuzzy Decisions ELICIT OWA aggregation Directed Acyclic Graph RNN LSTM GRU Type-2 Fuzzy Decision Evidential Reasoning algorithm and robust optimisation algorithms. This book is essential for anyone interested in the intersection of machine learning and service computing in complex decision-making systems. | Advances in Complex Decision Making Using Machine Learning and Tools for Service-Oriented Computing

GBP 44.99
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