Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
Автор: Laborde Gant Название: Learning Tensorflow.Js: Machine Learning in JavaScript ISBN: 1492090794 ISBN-13(EAN): 9781492090793 Издательство: Wiley Рейтинг: Цена: 47510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Holdroyd Tony Название: TensorFlow 2.0 Quick Start Guide ISBN: 178953075X ISBN-13(EAN): 9781789530759 Издательство: Неизвестно Рейтинг: Цена: 40450.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.
Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.
Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. The book also explores new approaches for integrating data privacy into machine learning pipelines.
Understand the machine learning management lifecycle
Implement data pipelines with Apache Airflow and Kubeflow Pipelines
Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform
Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement
Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js
Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated
Design model feedback loops to increase your data sets and learn when to update your machine learning models
Автор: Cai Shanqing, Bileschi Stan, Nielsen Eric Название: Deep Learning with JavaScript: Neural Networks in Tensorflow.Js ISBN: 1617296171 ISBN-13(EAN): 9781617296178 Издательство: Неизвестно Рейтинг: Цена: 61290.00 T Наличие на складе: Невозможна поставка. Описание: 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. 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 Fran ois 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
Автор: Capelo Luis Название: Beginning Application Development with TensorFlow and Keras ISBN: 1789537290 ISBN-13(EAN): 9781789537291 Издательство: Неизвестно Рейтинг: Цена: 18380.00 T Наличие на складе: Нет в наличии. Описание: With this book, you`ll learn how to train, evaluate and deploy Tensorflow and Keras models as real-world web applications. After a hands-on introduction, you`ll use a sample model to explore the details of deep learning, selecting the right layers that can solve a given problem. By the end of the book, you`ll build a Bitcoin application that ...
Автор: Kar Krishnendu Название: Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques ISBN: 1838827064 ISBN-13(EAN): 9781838827069 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.
Автор: Jain Ankit, Fandango Armando, Kapoor Amita Название: Tensorflow Machine Learning Projects ISBN: 1789132215 ISBN-13(EAN): 9781789132212 Издательство: Неизвестно Рейтинг: Цена: 47810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book will show you how to take advantage of TensorFlow`s most appealing features - simplicity, efficiency, and flexibility - in various scenarios. You will gain cutting-edge insights into using TensorFlow`s offerings for your problems and learn practical hacks to successfully implement real-world machine learning projects.
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 knowledge as a web developer, you'll add a whole new field of development to your tool 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.
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.
What You'll Learn
Use the JavaScript framework for ML
Build machine learning applications for the web
Develop dynamic and intelligent web content
Who This Book Is For
Web developers and who want a hands-on introduction to machine learning in JavaScript. A working knowledge of the JavaScript language is recommended.
Автор: Balakrishnan Kaushik Название: TensorFlow Reinforcement Learning Quick Start Guide ISBN: 1789533589 ISBN-13(EAN): 9781789533583 Издательство: Неизвестно Рейтинг: Цена: 33090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and ...
Автор: Sarang Poornachandra Название: Artificial Neural Networks with Tensorflow 2: Ann Architecture Machine Learning Projects ISBN: 1484261496 ISBN-13(EAN): 9781484261491 Издательство: Springer Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what`s new in TensorFlow 2, you`ll dive right into developing machine learning models through applicable projects.
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