Deep Learning With Javascript

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Deep Learning With Javascript

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Author : Stanley Bileschi
language : en
Publisher: Simon and Schuster
Release Date : 2020-01-24

Deep Learning With Javascript written by Stanley Bileschi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-24 with Computers categories.

Summary Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Foreword by Nikhil Thorat and Daniel Smilkov. About the technology Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack. About the book In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation. What's inside - Image and language processing in the browser - Tuning ML models with client-side data - Text and image creation with generative deep learning - Source code samples to test and modify About the reader For JavaScript programmers interested in deep learning. About the author Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet. TOC: PART 1 - MOTIVATION AND BASIC CONCEPTS 1 • Deep learning and JavaScript PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS 2 • Getting started: Simple linear regression in TensorFlow.js 3 • Adding nonlinearity: Beyond weighted sums 4 • Recognizing images and sounds using convnets 5 • Transfer learning: Reusing pretrained neural networks PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS 6 • Working with data 7 • Visualizing data and models 8 • Underfitting, overfitting, and the universal workflow of machine learning 9 • Deep learning for sequences and text 10 • Generative deep learning 11 • Basics of deep reinforcement learning PART 4 - SUMMARY AND CLOSING WORDS 12 • Testing, optimizing, and deploying models 13 • Summary, conclusions, and beyond

Hands On Machine Learning With Javascript

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Author : Burak Kanber
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-29

Hands On Machine Learning With Javascript written by Burak Kanber and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-29 with Computers categories.

A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning Book Description In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. What you will learn Get an overview of state-of-the-art machine learning Understand the pre-processing of data handling, cleaning, and preparation Learn Mining and Pattern Extraction with JavaScript Build your own model for classification, clustering, and prediction Identify the most appropriate model for each type of problem Apply machine learning techniques to real-world applications Learn how JavaScript can be a powerful language for machine learning Who this book is for This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book.

Deep Learning With Javascript

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Author :
language : en
Publisher: Independently Published
Release Date : 2020-06-29

Deep Learning With Javascript written by and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with categories.

Deep Learning with Javascript provides a beginners guide to getting started with neural networks. Through a hands-on approach the book provides a wide-range of interactive examples that the user is able to customize and expand to help them understand the concepts in a real-world sense. Using example-based learning and projects. The book has been formatted and designed with sample listings and support material, so whether or not you are currently an expert in Javascript and neural network development, actively working with an existing framework, or completely in the dark about this mysterious topic, this book has something for you. If you're an experienced developer, you'll find this book a light refresher to the subject, and if you're deciding whether or not to delve into web-based games, this book may help you make that significant decision. The book introduces core mathematic fundamentals in addition to projects and exercises such as feature detection, inverse kinematics, text extaction from handwritten sources and much more. The text is organised to guide the reader through the exciting topic of deep learning from a ground-up hands-on perspective. Organized around the browser-based language (Javascript) in combination with popular open source libraries (Synaptic), the book includes numerous simplified practical examples in the body of the text as well as technical explanations on limitations and engineering workarounds.

Learning Tensorflow Js

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Author : Gant Laborde
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-10

Learning Tensorflow Js written by Gant Laborde and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Computers categories.

Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learning and the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch

Deep Learning With Javascript

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Author : Andrew Davis
language : en
Publisher: Independently Published
Release Date : 2024-11-15

Deep Learning With Javascript written by Andrew Davis and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-15 with Computers categories.

In a world where artificial intelligence is transforming industries, Deep Learning with JavaScript brings the power of deep neural networks directly to web developers and JavaScript enthusiasts. This hands-on guide will teach you how to construct, train, and deploy sophisticated neural networks using JavaScript, TensorFlow.js, and Brain.js-all within a browser environment. Whether you're a seasoned web developer or a curious coder, this book provides the skills to develop real-time AI-driven applications, from image recognition to interactive web experiences, without needing Python or extensive server resources. Learn to optimize JavaScript for scalable, efficient deep learning projects and create solutions that run seamlessly across platforms. With this book, you'll go beyond static code to build dynamic, scalable applications that redefine what's possible in web-based AI. Start your journey into the heart of deep learning and become equipped with the tools to bring cutting-edge neural networks to life in JavaScript.

Javascript Programming For Building Deep Learning Models

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Author : AGENTA. MILLO
language : en
Publisher: Independently Published
Release Date : 2025-03-10

Javascript Programming For Building Deep Learning Models written by AGENTA. MILLO and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-10 with Computers categories.

Unlock the immense power of Artificial Intelligence with JavaScript! JavaScript Programming for Building Deep Learning Models is the ultimate guide to mastering AI development using JavaScript. This comprehensive book empowers developers with the tools and knowledge needed to create advanced deep learning models, all while leveraging JavaScript's unparalleled accessibility and web integration. Dive deep into the world of AI and explore its potential with real-world applications. From neural networks to computer vision, NLP, and reinforcement learning, this book takes you step-by-step through essential concepts and complex models. Learn to build and train models using TensorFlow.js, and discover how to bring powerful AI to web browsers and edge devices for real-time, client-side applications. Whether you're building your first neural network or deploying sophisticated deep learning solutions, this book will guide you every step of the way. With hands-on projects and practical exercises, you'll develop expertise in the most cutting-edge AI techniques. In this book, you will: Master the fundamentals of deep learning, neural networks, and their applications. Discover why JavaScript is a game-changer in AI development. Explore advanced deep learning models, including CNNs, RNNs, GANs, and Transformers. Learn how to create real-time applications like image recognition, chatbots, and even self-learning systems. Implement deep learning with TensorFlow.js to run models directly in web browsers or Node.js environments. Optimize and scale models for real-world applications, from edge computing to cloud deployment. Perfect for developers eager to harness the power of JavaScript for AI, this book provides both foundational knowledge and cutting-edge techniques for building AI-powered systems. Start your journey into the world of deep learning today and unlock the potential of AI in web development with JavaScript! Why Choose This Book? Practical, hands-on approach with code examples and exercises Clear explanations for both beginners and advanced learners Insights on optimizing performance and scaling AI models Real-world applications, including NLP, image recognition, and chatbots Learn to deploy AI models directly to browsers and edge devices Prepare to push the boundaries of AI development with JavaScript Programming for Building Deep Learning Models!

Practical Machine Learning In Javascript

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Author : Charlie Gerard
language : en
Publisher:
Release Date : 2021

Practical Machine Learning In Javascript written by Charlie Gerard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Build machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. You'll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your already honed skills as a web developer, you'll add a whole new field of development to your skill set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically. Get started in machine learning with web technologies. Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js-an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices. You will: Use the JavaScript framework for ML Build machine learning applications for the web Develop dynamic and intelligent web content.

Artificial Intelligence With Javascript

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Author : Andrew Davis
language : en
Publisher: Independently Published
Release Date : 2024-10-08

Artificial Intelligence With Javascript written by Andrew Davis and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Computers categories.

Unlock the power of deep learning and artificial intelligence using the language you already know and love - JavaScript! This groundbreaking book bridges the gap between web development and cutting-edge AI, empowering you to create intelligent applications that can transform user experiences across platforms. Dive into the world of neural networks, machine learning algorithms, and AI-driven decision-making, all through the lens of JavaScript. Learn how to: Implement deep learning models directly in your web and mobile applications Harness the potential of natural language processing and computer vision using JavaScript libraries Optimize performance and scalability for AI-driven applications in browser environments Create intelligent chatbots, recommendation systems, and predictive analytics tools Whether you're a seasoned JavaScript developer looking to expand your skillset or an AI enthusiast eager to bring your models to life on the web, this book provides the perfect blend of theory and practical, hands-on examples. Don't let the misconception that AI is only for Python developers hold you back. Embrace the future of web-based artificial intelligence and start building smarter, more responsive applications today!

Practical Tensorflow Js

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Author : Juan De Dios Santos Rivera
language : en
Publisher: Apress
Release Date : 2020-10-03

Practical Tensorflow Js written by Juan De Dios Santos Rivera and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-03 with Computers categories.

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.​js​ is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard​, ​ml5js​, ​tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.​js​ to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.

Deep Learning For Image Processing Applications

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Author : D.J. Hemanth
language : en
Publisher: IOS Press
Release Date : 2017-12

Deep Learning For Image Processing Applications written by D.J. Hemanth and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12 with Computers categories.

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.