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Название: Deep Learning Technologies for Social Impact
Автор: Shajulin Benedict
Издательство: IOP Publishing
Серия: IOP Series in Next Generation Computing
Год: 2022
Страниц: 267
Язык: английский
Формат: pdf (true), epub
Размер: 33.8 MB

Artificial Intelligence (AI) is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to these ever-present problems. Deep Learning (DL) techniques have increased in power in recent years, with algorithms already exhibiting tremendous possibilities in domains such as scientific research, agriculture, smart cities, finance, healthcare, conservation, the environment, industry and more. In particular, the book explores and emphasises techniques involved in DL such as image classification, image enhancement, word analysis, human–machine emotional interfaces and the applications of these techniques for smart cities and societal problems. To extend the theoretical description, the book is enhanced through case studies, including those implemented using Tensorflow2 and relevant IoT-specific sensor/actuator frameworks. Ease of operation—the TensorFlow platform delivers easy integration with several other platforms, such as Google run time collaboration services or locally connected clusters. Users have to select appropriate compute resources using the platform in order to execute deep learning services in the cloud.

Название: jаvascript для начинающих, 6-е издание
Автор: Майк МакГрат
Издательство: Эксмо
Год: 2023
Формат: pdf
Страниц: 232
Размер: 16,4 Мб
Язык: русский

Цветное руководство по jаvascript для начинающих позволит в короткое время освоить этот язык программирования и приступить к созданию красивых и функциональных сайтов. Вся информация представлена схематично и снабжена наглядными примерами, а код и другие элементы, необходимые для обучения, читатели могут скачать и использовать совершенно бесплатно.

Название: "Поколение Python": курс для профессионалов
Автор: Тимур Гуев, Артур Харисов
Издательство: Stepik
Год: 2022
Формат: PDF
Страниц: много
Размер: 57 Mb
Язык: Русский

В курсе рассматриваются даты и время, дополнительные типы коллекций, итераторы, генераторы, декораторы, рекурсия, исключения, регулярные выражения и многое другое.

Название: Getting to Know IntelliJ IDEA : Level up your IntelliJ IDEA knowledge so that you can focus on doing what you do best
Автор: Trisha Gee, Helen Scott
Издательство: Leanpub
Год: 2022-11-06
Страниц: 380
Язык: английский
Формат: pdf (true)
Размер: 129.4 MB

If we treat our IDE as a text editor, we are doing ourselves a disservice. Using a combination of tutorials and a questions-and-answers approach, Getting to Know IntelliJ IDEA will help you find ways to use IntelliJ IDEA that enable you to work comfortably and productively as a professional developer. This book is for developers using IntelliJ IDEA, whether you’re just beginning or have been using it for a while. If you’re just beginning with IntelliJ IDEA, we’ll take you on the journey of learning the tool quickly and efficiently to help you be as productive as possible. If you’ve been using IntelliJ IDEA a while, then we’ll help you expand your horizons and show you some cool tricks which will improve your productivity. At the top level, this book is primarily aimed at Java developers who use, or want to use, IntelliJ IDEA, but anyone who uses an IntelliJ Platform IDE (for example, Webstorm, PyCharm, Rider) should pick up plenty of tips.

Название: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances
Автор: Yanan Sun, Gary G. Yen, Mengjie Zhang
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2023
Страниц: 335
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks (DNN). The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

Название: Explainable Edge AI: A Futuristic Computing Perspective
Автор: Aboul Ella Hassanien, Deepak Gupta, Anuj Kumar Singh
Издательство: Springer
Год: 2023
Страниц: 187
Язык: английский
Формат: pdf (true), epub
Размер: 19.4 MB

This book presents explainability in edge AI, an amalgamation of edge computing and AI. The issues of transparency, fairness, accountability, explainability, interpretability, data-fusion, and comprehensibility that are significant for edge AI are being addressed in this book through explainable models and techniques. The concept of explainable edge AI is new in front of the academic and research community, and consequently, it will undoubtedly explore multiple research dimensions. The book presents the concept of explainability in edge AI which is the amalgamation of edge computing and AI. In the futuristic computing scenario, the goal of explainable edge AI will be to execute the AI tasks and produce explainable results at the edge. First, this book explains the fundamental concepts of explainable artificial intelligence (XAI), then it describes the concept of explainable edge AI, and finally, it elaborates on the technicalities of explainability in edge AI. Owing to the quick transition in the current computing scenario and integration with the latest AI-based technologies, it is significant to facilitate people-centric computing through explainable edge AI. The proposed protocol is implemented using TensorFlow and Keras libraries in Python language.

Название: Deep Learning: From Big Data to Artificial Intelligence with R
Автор: Dr. Stéphane Tufféry
Издательство: Wiley
Год: 2023
Страниц: 542
Язык: английский
Формат: pdf (true)
Размер: 10.9 MB

A concise and practical exploration of key topics and applications in Data Science. In Deep Learning: From Big Data to Artificial Intelligence with R , expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. Classroom-tested and intuitively organized, Deep Learning: From Big Data to Artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book.

Название: Machine Learning on Commodity Tiny Devices: Theory and Practice
Автор: Song Guo, Qihua Zhou
Издательство: CRC Press
Год: 2023
Страниц: 268
Язык: английский
Формат: pdf (true)
Размер: 30.3 MB

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system.

Название: Building Feature Extraction with Machine Learning: Geospatial Applications
Автор: Bharath H. Aithal, Prakash P.S.
Издательство: CRC Press
Год: 2023
Страниц: 145
Язык: английский
Формат: pdf (true)
Размер: 13.2 MB

Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and Machine Learning (ML) is the science that supports it. This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others. Explains the methods for estimating object height from optical satellite remote sensing images using Python.

Название: Normalization Techniques in Deep Learning
Автор: Lei Huang
Издательство: Springer
Год: 2022
Страниц: 117
Язык: английский
Формат: pdf (true), epub
Размер: 13.5 MB

This book presents and surveys normalization techniques with a deep analysis in training deep neural networks. In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks. Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures. The author provides guidelines for elaborating, understanding, and applying normalization methods. This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks. The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.