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    Designing Autonomous AI

    $32.24 Add to cart

    Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn’t learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You’ll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs

  • Learn AI-assisted Python Programming: With GitHub Copilot and ChatGPT

    $4.99 Add to cart

    Writing computer programs in Python just got a lot easier! Use AI-assisted tools like GitHub Copilot to go from idea to application faster than you can say “ChatGPT.”

    In Learn AI-Assisted Python Programming: With Copilot and ChatGPT you’ll learn how to:

    Write fun and useful Python applications—no programming experience required!
    Use the Copilot AI coding assistant to create Python programs
    Write prompts that tell Copilot exactly what to do
    Read Python code and understand what it does
    Test your programs to make sure they work the way you want them to
    Fix code with prompt engineering or human tweaks
    Apply Python creatively to help out on the job

    Learn AI-Assisted Python Programming: With Copilot and ChatGPT is a beginner’s guide that embraces AI as the future of coding. AI-assisted coding tools like GitHub Copilot and ChatGPT empower you to create useful Python applications without learning all the low-level details of a programming language. You’ll hit the ground running as you write prompts that tell your AI-assistant exactly what you want your programs to do. Along the way, you’ll pick up the essentials of Python programming and practice the higher-level thinking you’ll need to create working apps for data science, automation, and even video games.

    Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

    About the Technology

    AI has changed the way we write computer programs. With tools like Copilot and ChatGPT, you can describe what you want in plain English, and watch your AI assistant generate the code right before your eyes. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming.

    About the book

    Learn AI-Assisted Python Programming: With Copilot and ChatGPT teaches you to code the AI way. Instead of starting with slow, low-level details, you’ll learn how to bring your ideas to life immediately using AI-generated code. You’ll practice the new essentials, like prompt engineering, code reading, and AI-assisted testing and program design. As you go, you’ll absorb the basics of Python programming so you can understand and improve your programs. You’ll quickly write small-but-useful Python programs for data visualization, automation, and more. Absolutely no programming experience required!

    About the reader

    If you can move files around on your computer and open a web browser, you can learn to write Python programs with this book!

    About the author

    Dr. Leo Porter is an Associate Teaching Professor of computer science at UC San Diego. He has over a decade of teaching experience and is well-known for his award-winning research on effective pedagogies and assessments in computer science.

    Dr. Daniel Zingaro is an Associate Teaching Professor of computer science and award-winning teacher at the University of Toronto. His main area of research is computer science education research, where he studies how students learn computer science material.

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    ChatGPT for Dummies

    $9.00 Add to cart

    Learn how the disruptive AI chatbot is going to change school, work, and beyond

    ChatGPT For Dummies demystifies the artificial intelligence tool that can answer questions, write essays, and generate just about any kind of text it’s asked for. This powerful example of generative AI is widely predicted to upend education and business. In this book, you’ll learn how ChatGPT works and how you can operate it in a way that yields satisfactory results. You’ll also explore the ethics of using AI-generated content for various purposes. Written by a journalist who’s been on the front lines of artificial intelligence for over a decade, this book dives deep into ChatGPT’s potential, so you can make informed decisions—without asking ChatGPT for help.

    Learn how ChatGPT works and how it fits into the world of generative AI
    Harness the power of ChatGPT to help you, and avoid letting it hinder you
    Write queries that deliver the kind of response you want
    Take a look into how the ChatGPT API interacts with other tools and platforms

    This just-in-time Dummies title is perfect for any life or career may be impacted by ChatGPT and other AI. ChatGPT is just the tip of the iceberg, and this book can help you prepare for the future.

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    Visualizing Data in R 4

    $33.74 Add to cart

    Master the syntax for working with R’s plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This book will take advantage of the new features added to R 4 where appropriate including a refreshed color palette for charts, Cairo graphics with more fonts/symbols, and improved performance from grid graphics including ggplot 2 rendering speed. Visualizing Data in R 4 starts with an introduction and then is split into two parts and six appendices. Part I covers the function plot() and the ancillary functions you can use with plot(). You’ll also see the functions par() and layout(), providing for multiple plots on a page. Part II goes over the basics of using the functions qplot() and ggplot() in the package ggplot2. The default plots generated by the functions qplot() and ggplot() give more sophisticated-looking plots than the default plots done by plot() and are easier to use, but the function plot() is more flexible. Both plot() and ggplot() allow for many layers to a plot. The six appendices will cover plots for contingency tables, plots for continuous variables, plots for data with a limited number of values, functions that generate multiple plots, plots for time series analysis, and some miscellaneous plots. Some of the functions that will be in the appendices include functions that generate histograms, bar charts, pie charts, box plots, and heatmaps. What You Will Learn • Use R to create informative graphics • Master plot(), qplot(), and ggplot() • Discover the canned graphics functions in stats and graphics Format plots generated by plot() and ggplot() Who This Book Is For Those in data science who use R. Some prior experience with R or data science is recommended.

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    Trends and Advancements of Image Processing and Its Applications

    $55.87 Add to cart

    This book covers current technological innovations and applications in image processing, introducing analysis techniques and describing applications in remote sensing and manufacturing, among others. The authors include new concepts of color space transformation like color interpolation, among others. Also, the concept of Shearlet Transform and Wavelet Transform and their implementation are discussed. The authors include a perspective about concepts and techniques of remote sensing like image mining, geographical, and agricultural resources. The book also includes several applications of human organ biomedical image analysis. In addition, the principle of moving object detection and tracking — including recent trends in moving vehicles and ship detection – is described. • Presents developments of current research in various areas of image processing; • Includes applications of image processing in remote sensing, astronomy, and manufacturing; • Pertains to researchers, academics, students, and practitioners in image processing.

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    Transactional Machine Learning With Data Streams and AutoML

    $33.74 Add to cart

    Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. What You Will Learn • Discover transactional machine learning Measure the business value of TML • Choose TML use cases • Design technical architecture of TML solutions with Apache Kafka • Work with the technologies used to build TML solutions • Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud Who This Book Is For Data scientists, machine learning engineers and architects, and AI and machine learning business leaders.

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    Practical Computer Vision With SimpleCV

    $11.24 Add to cart

    Learn how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open source framework written in Python. Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images. You’ll then learn how to apply these methods with SimpleCV, using sample Python code. All you need to get started is a Windows, Mac, or Linux system, and a willingness to put CV to work in a variety of ways. Programming experience is optional. Capture images from several sources, including webcams, smartphones, and Kinect Filter image input so your application processes only necessary information Manipulate images by performing basic arithmetic on pixel values Use feature detection techniques to focus on interesting parts of an image Work with several features in a single image, using the NumPy and SciPy Python libraries Learn about optical flow to identify objects that change between two image frames Use SimpleCV’s command line and code editor to run examples and test techniques

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    Practical Simulations for Machine Learning

    $32.24 Add to cart

    Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models. That—s just the beginning. With this practical book, you’ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You’ll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits

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