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Authors: Huixiao Hong (2023) - This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students.
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Authors: David Foster (2023) - The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.
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Authors: Arash Gharehbagh (2023) - The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level
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Authors: Zian Wang; Andre Ye (2023) - Who This Book Is For Data scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security
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Authors: Daniel P. Friedman (2023) - A gentle but detailed introduction to some of the algorithmic ideas behind machine learning
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Authors: Kaige Zhang (2023) - With the development of artificial intelligence (AI), the deep learning technique has achieved great success. However, using deep learning for high accurate crack localization is non-trivial. Based on deep learning, this book has solved a bunch of important issues existing in crack-like object detection, and finished a practical smart pavement surface inspection system. By introducing those method and the system, this book gives the reader an easy way to get into the computer vision and deep learning research area. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection w...
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Authors: Grigory Sapunov (2023) - Deep Learning with JAX teaches you how to use JAX and its ecosystem to build neural networks. You’ll learn by exploring interesting examples including an image classification tool, an image filter application, and a massive scale neural network with distributed training across a cluster of TPUs. Discover how to work with JAX for hardware and other low-level aspects and how to solve common machine learning problems with JAX. By the time you’re finished with this awesome book, you’ll be ready to start applying JAX to your own research and prototyping!
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Authors: Duc Haba (2023) - This book is for data scientists and students interested in the AI discipline. Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book
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Authors: Fei Hu; Iftikhar Rasheed (2023) - Deep Learning (DL) will be an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart & efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for ve...
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Authors: L. Ashok Kumar (2023) - This book delves into issues of natural language processing, a subset of artificial intelligence that enables computers to understand the meaning of human language using techniques of machine learning and deep learning algorithms to discern a words' semantic meanings
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Authors: Roshani Raut (2023) - This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A generative adversarial network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are vario...
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Authors: Lanier, Lee (2015) - In Compositing Visual Effects in After Effects, industry veteran Lee Lanier covers all the common After Effects techniques any serious visual effects artist needs to know, combining the latest, professionally-vetted studio practices and workflows with multi-chapter projects and hands-on lessons.
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Authors: Wickham, Hadley (2023) - This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience
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Authors: Vanderplas, Jacob T (2023) - Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools
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Authors: Pumperla, Max (2023) - Get started with Ray, the open source distributed computer framework that simplifies the process of scaling comute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin with compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.
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Authors: Jarmul, Katharine (2023) - Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.
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Authors: Stauffer, Matt (2023) - The third edition of this practical guide provides the definitive introduction to one of today's most popular web frameworks.
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Authors: Loy, Marc (2023) - This guide helps you: Learn the structure of the Java language and Java applications Write, compile, and execute Java applications Understand the basics of Java threading and concurrent programming Learn Java I/O basics, including local files and network resources Create compelling interfaces with an eye toward usability Learn how functional features have been integrated in Java Keep up with Java developments as new versions are released
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Authors: Hall, Patrick (2023) - This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
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Authors: Bloom, David E (2023) - "Drawing on an international pool of scholars, this cutting-edge Handbook surveys the micro, macro and institutional aspects of the economics of ageing. Structured in seven parts, the volume addresses a broad range of themes, including health economics, labour economics, pensions and social security, generational accounting, wealth inequality and regional perspectives. Each chapter combines a succinct overview of the state of current research with a sketch of a promising future research agenda. This Handbook will be an essential resource for advanced students, researchers and policymakers looking at the economics of ageing across the disciplines of economics, demography, public policy...
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