Javascript Programming For Building Deep Learning Models With Deeplearn Js And Tensorflow Js
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Javascript Programming For Building Deep Learning Models With Deeplearn Js And Tensorflow Js
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Author : Cesar J Burnett
language : en
Publisher: Independently Published
Release Date : 2025-08-05
Javascript Programming For Building Deep Learning Models With Deeplearn Js And Tensorflow Js written by Cesar J Burnett and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-05 with Computers categories.
Have you ever wondered how your favorite websites seem to read your mind, or how a simple text box can write a story for you? We've all seen the magic of AI, but the journey from a brilliant idea to a working application can feel like a steep, technical mountain. Most books on deep learning are locked behind complex languages and intimidating math, leaving countless creators and web developers on the sidelines. This book is your all-access pass. It cuts through the jargon, empowering you with the tools you already know-JavaScript-to build, train, and deploy powerful AI models. You'll go from understanding core concepts to creating real, hands-on applications, including image classifiers, live webcam tools, and even recommendation systems. This is more than just theory; it's a practical, step-by-step guide that puts the power of deep learning directly into your hands. Ready to build the future of intelligent web applications? Your journey starts now.
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!
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
Deep Learning With Tensorflow And Keras
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Author : Amita Kapoor
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-06
Deep Learning With Tensorflow And Keras written by Amita Kapoor 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 2022-10-06 with Computers categories.
Build cutting edge machine and deep learning systems for the lab, production, and mobile devices Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook Description Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML. What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is for This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems. Some machine learning knowledge would be useful. We don't assume TF knowledge.
Hands On Machine Learning With Tensorflow Js
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Author : Kai Sasaki
language : en
Publisher:
Release Date : 2019-11-27
Hands On Machine Learning With Tensorflow Js written by Kai Sasaki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-27 with Computers categories.
Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js Create smart web applications from scratch with the help of useful examples Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function Book Description TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge. By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance. What you will learn Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js Apply the Bellman equation to solve MDP problems Use the k-means algorithm in TensorFlow.js to visualize prediction results Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps Implement tf.js backend frameworks to tune and accelerate app performance Who this book is for This book is for web developers who want to learn how to integrate machine learning techniques with web-based applications from scratch. This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow.js. Working knowledge of JavaScript programming language is all you need to get started.
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.
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 In Visual Computing And Signal Processing
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Author : Krishna Kant Singh
language : en
Publisher: CRC Press
Release Date : 2022-10-20
Deep Learning In Visual Computing And Signal Processing written by Krishna Kant Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-20 with Technology & Engineering categories.
An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this new volume covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more.
Javascript Programming For Machine Learning With Tensorflow Js And Brain Js
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Author : Michael A Champagne
language : en
Publisher: Independently Published
Release Date : 2025-06-22
Javascript Programming For Machine Learning With Tensorflow Js And Brain Js written by Michael A Champagne and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-22 with Computers categories.
Unleash AI in the Browser: Your Passport to Intelligent JavaScript Systems Dive into the future of web development with "JavaScript Programming for Machine Learning with TensorFlow.js and Brain.js." This book is your comprehensive guide to crafting intelligent, real-time applications, bridging the gap between cutting-edge AI and the ubiquitous power of JavaScript. From mastering the fundamentals of neural networks to deploying models directly on the web or in Node.js, you'll gain the skills to build dynamic, privacy-conscious, and performant AI solutions that truly engage users and adapt to the digital world - anywhere, from USA to London. Start your journey to becoming a full-stack AI innovator today.
Javascript Programming For Machine Learning
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Author : AGENTA. MILLO
language : en
Publisher: Independently Published
Release Date : 2025-03-06
Javascript Programming For Machine Learning 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 2025-03-06 with Computers categories.
Unlock the power of Artificial Intelligence (AI) and Machine Learning (ML) with JavaScript Programming For Machine Learning: Develop Efficient and Effective AI Web Applications with JS! Perfect for developers and enthusiasts ready to dive into the world of machine learning, this comprehensive guide empowers you to build intelligent web applications using JavaScript, all without relying on heavy backend systems. This book demystifies the intricacies of machine learning, offering you a step-by-step approach to mastering essential ML concepts, from data handling and training models to deploying them in the browser. Explore hands-on projects like sentiment analysis apps, image classification systems, and even voice recognition assistants-all powered by popular JavaScript libraries like TensorFlow.js, Brain.js, and Synaptic.js. Inside, you'll discover: How to leverage JavaScript's growing ecosystem for ML with real-world applications. Efficient techniques for data collection, preprocessing, and transformation to create high-performing models. Practical guides to integrating cutting-edge ML models into dynamic web applications. Detailed coverage of advanced topics such as reinforcement learning, deep learning, and model optimization. Whether you're building smart recommendation systems or enhancing user experiences with AI, JavaScript Programming For Machine Learning equips you with the tools and knowledge to create seamless, scalable, and real-time AI-powered solutions-all within the browser. Perfect for developers seeking to harness the power of JavaScript for next-level web applications, this book will have you building powerful AI projects in no time. Grab your copy today and start programming the future of AI in the browser!