Название: Machine Learning: Theory and Practice
Автор: Jugal Kalita
Издательство: CRC Press
Год: 2023
Страниц: 299
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Machine Learning: Theory and Practice provides an introduction to the most popular methods in Machine Learning (ML). The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. These days, the predominant programming languages used in academia as well as industry, are Python and R. R is a statistical package, with an associated language that has powerful libraries for all kinds of machine learning tasks. We will also discuss specific libraries like TensorFlow and Keras in the context of deep learning. We include examples in R in the current edition of the book.

Название: Artificial Intelligence, Big Data and Data Science in Statistics
Автор: Ansgar Steland, Kwok-Leung Tsui
Издательство: Springer
Год: 2022
Страниц: 378
Язык: английский
Формат: pdf (true)
Размер: 11.0 MB

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how Data Science is a combination of statistics, computational science, and Machine Learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.

Название: Machine Learning Algorithms: Adversarial Robustness in Signal Processing
Автор: Fuwei Li, Lifeng Lai, Shuguang Cui
Издательство: Springer
Год: 2022
Страниц: 109
Язык: английский
Формат: pdf (true), epub
Размер: 10.4 MB

This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of Machine Learning algorithms in signal processing under adversarial attacks. This book targets researchers working in Machine Learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in Machine Learning, adversarial Machine Learning, robust Machine Learning, and technical consultants working on the security and robustness of Machine Learning are likely to purchase this book as a reference guide.

Название: The Beginner's Guide to Data Science
Автор: Robert Ball, Brian Rague
Издательство: Springer
Год: 2022
Страниц: 251
Язык: английский
Формат: pdf (true), epub
Размер: 26.2 MB

This book discusses the principles and practical applications of Data Science, addressing key topics including data wrangling, statistics, Machine Learning (ML), data visualization, natural language processing (NLP) and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered. There are many Python example programs used throughout this book. We have done our best to make these examples readily available at a separate downloadable location. In addition, almost every image used in this book was created by Python code. For these plots and diagrams, the source code designed to create the image is cited and available.

Название: An Introduction to Statistics with Python: With Applications in the Life Sciences, 2nd Edition
Автор: Thomas Haslwanter
Издательство: Springer
Год: 2022
Страниц: 341
Язык: английский
Формат: pdf (true)
Размер: 10.05 MB

Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs. The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader’s immediate understanding of the topic. Accompanying data sets and Python programs are also available online.

Название: Classification Applications with Deep Learning and Machine Learning Technologies
Автор: Laith Abualigah
Издательство: Springer
Год: 2023
Страниц: 287
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB

This book is very beneficial for early researchers/faculty who want to work in Deep Learning and Machine Learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies’ image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development.

Название: Introduction to Infrastructure as Code: A Brief Guide to the Future of DevOps
Автор: Sneh Pandya, Riya Guha Thakurta
Издательство: Apress
Год: 2022
Страниц: 196
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB

Get inspired to explore the depths of the DevOps field. In today’s rapidly transforming world, Infrastructure as Code (IaC) has emerged as an effective approach to maintain, scale, and deploy software systems. This book offers a mixture of foundational IaC concepts and practical examples to give you hands-on experience. You will first gain an understanding of DevOps culture as well as how to adapt to IaC. Introduction to Infrastructure as Code begins by reviewing the innovative features that DevOps in general, and IaC in particular, have to offer for adoption and growth for different verticals. With this solid base established, you will then learn the importance, processes, and outcome of building infrastructure solutions.

Название: Solving Identity Management in Modern Applications: Demystifying OAuth 2, OpenID Connect, and SAML 2, Second Edition
Автор: Yvonne Wilson, Abhishek Hingnikar
Издательство: Apress
Год: 2023
Страниц: 398
Язык: английский
Формат: pdf (true), epub
Размер: 10.2 MB

Know how to design and use identity management to protect your application and the data it manages. At a time when security breaches result in increasingly onerous penalties, it is paramount that application developers and owners understand identity management and the value it provides when building applications. This book takes you from account provisioning to authentication to authorization, and covers troubleshooting and common problems to avoid. The authors include predictions about why this will be even more important in the future. Application best practices with coding samples are provided.

Название: Mastering Game Design with Unity 2021: Immersive Workflows, Visual Scripting, Physics Engine, GameObjects, Player Progression, Publishing, and a Lot More
Автор: Scott Tykoski
Издательство: BPB Publications
Год: 2023
Страниц: 394
Язык: английский
Формат: epub (true)
Размер: 18.99 MB

Learn and Leverage the Power of Unity to Create Amazing Video Games! The Unity Engine has been steadily evolving over the past few years into one of the most powerful resources for the game development community. Its feature-rich toolkit and user-friendliness make it an ideal foundation for budding game developers. The book ‘Mastering Game Design with Unity 2021’ will walk you through creating a multimedia game from scratch, covering everything from the basics of game development to advanced design concepts. The book will help you to learn the ins and outs of scenes, game objects, input systems, physics, particles, and post-processing effects, and even get access to instructions to put your newfound skills to use. In addition, this book will help you to learn the fundamentals of game logic design, interactive narratives, game mechanics, storyboarding, and design structure in an easy-to-understand format from a coaching game expert.

Название: Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, 2nd Edition (5th Early Release)
Автор: David Foster
Издательство: O’Reilly Media, Inc.
Год: 2022-11-16
Страниц: 286
Язык: английский
Формат: epub
Размер: 42.6 MB

Generative modeling is one of the hottest topics in AI. It is now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, Machine Learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative Deep Learning models such as variational autoencoders, generative adversarial networks (GANs), Transformers, normalizing flows, and diffusion models. A generative model can be broadly defined as follows: Generative modeling is a branch of Machine Learning (ML) that deals with the creation of models that can generate new data points that are similar to the training data. We use the Keras function image_dataset_from_directory to create a Tensorflow Dataset pointed at the directly where the images are stored.