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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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Authors: Farmer, Donald (2023) - This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations. Author Donald Farmer, principal of TreeHive Strategy, shows business users how to improve decision-making without becoming analytic specialists.
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Authors: Pine, David (2023) - Take advantage of your C# skills to build UI components and client-side experiences with .NET. With this practical guide, you'll learn how to use Blazor WebAssembly to develop next-generation web experiences. Built on top of ASP.NET Core, Blazor represents the future of .NET single-page applications (SPA) investments. Author David Pine, who focuses on
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Authors: Weidig, Ben (2023) - ava developers usually tackle the complexity of software development through object-oriented programming (OOP). But not every problem is a good match for OOP. The functional programming (FP) paradigm offers you another approach to solving problems, and Java provides easy-to-grasp FP tools such as lambda expressions and Streams. If you're interested in applying FP concepts to your Java code, this book is for you.
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Authors: Sarkis, Anthony (2023) - Training Data controls the system by defining the ground truth goals for the creation of Machine Learning models. This involves technical representations, people decisions, processes, tooling, system design, and a variety of new concepts specific to Training Data. In a sense, a Training Data mindset is a paradigm upon which a growing list of theories, research and standards are emerging. A Machine Learning (ML) Model that is created as the end result of a ML Training Process.
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Authors: Ali Iranmanesh (2023) - This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.
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Authors: Gallatin, Kyle (2023) - This practical guide provides more than 200 self-contained recipes to help you solve Machine Learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks
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Authors: Hou, Zhe (2021) - This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started.
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Authors: Comito, Carmela (2022) - The book is intended to cover how the fusion of IoT and AI allows the design of models, methodologies, algorithms, evaluation benchmarks, and tools can address challenging problems related to health informatics, healthcare, and wellbeing.
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Authors: Kane, Sean (2023) - This edition includes significant updates to the examples and explanations that reflect the substantial changes that have occurred since Docker was first released almost a decade ago. Sean Kane and Karl Matthias have updated the text to reflect best practices and to provide additional coverage of new features like BuildKit, multi-architecture image support, rootless containers
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Authors: Hu, Fei (2023) - Today Artificial Intelligence (AI) and Machine/Deep Learning (ML/DL) have become the hottest areas in the information technology. In our society, there are so many intelligent devices that rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms/tools have used in many Internet applications and electronic devices, they are also vulnerable to various attacks and threats. The AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, and many other attacks/threats. Those attacks make the AI products dangerous to use
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Authors: Mishra, Pradeepta (2023) - What You Will Learn: Create code snippets and explain machine learning models using Python, Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale, Understand the different variants of neural network models
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