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.
Автор: Boschetti Alberto, Massaron Luca, Thakur Abhishek Название: Tensorflow Deep Learning Projects ISBN: 1788398068 ISBN-13(EAN): 9781788398060 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks` performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more.
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: Prakash Kolla Bhanu, Kanagachidambaresan G. R. Название: Programming with TensorFlow: Solution for Edge Computing Applications ISBN: 3030570797 ISBN-13(EAN): 9783030570798 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics.
Автор: Singh Ghotra Manpreet, Dua Rajdeep Название: Neural Network Programming with TensorFlow ISBN: 1788390393 ISBN-13(EAN): 9781788390392 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
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Автор: Suryadevara Nagender Kumar Название: Beginning Machine Learning in the Browser: Quick-Start Guide to Gait Analysis with JavaScript and Tensorflow.Js ISBN: 1484268423 ISBN-13(EAN): 9781484268421 Издательство: Springer Цена: 41920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: What is Machine Learning (ML)? Basics of Java Script (JS) Programming in the browser using Java Script Graphics and Interactive processing in the browser using Java Script libraries Getting started with P5.JS and ML5.JS References Chapter 2: Human Pose Estimation in the Browser Browser based data processing Posenet vs Openpose models Human pose estimation using ML5.Posenet Inputs, Outputs and Data structures of Posenet model References Chapter 3: Human Pose Classification Classification techniques using ML Neural Network in the browser Human Pose classification based on the outputs of Posenet model Consideration of poses using Confidence scores of Posenet model Storage of data using JSON formats related to the outputs of Posenet model References Chapter 4: Gait Analysis Normal vs Abnormal Gait patterns Determination of Gait patterns using threshold values of the models User Interface design and development for monitoring of Gait patterns Real-Time data visualization of the Gait patterns on the browser References Chapter 5: Future Possible Applications of Key Concepts
Автор: Gridin Название: Automated Deep Learning Using Neural Network Intelligence ISBN: 1484281489 ISBN-13(EAN): 9781484281482 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn * Know the basic concepts of optimization tuners, search space, and trials * Apply different hyper-parameter optimization algorithms to develop effective neural networks * Construct new deep learning models from scratch * Execute the automated Neural Architecture Search to create state-of-the-art deep learning models * Compress the model to eliminate unnecessary deep learning layers Who This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development
Автор: Mcclure, Nick Название: Tensorflow machine learning cookbook ISBN: 1786462168 ISBN-13(EAN): 9781786462169 Издательство: Неизвестно Рейтинг: Цена: 74780.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Explore machine learning concepts using the latest numerical computing library -- TensorFlow -- with the help of this comprehensive cookbook
Key Features
Your quick guide to implementing TensorFlow in your day-to-day machine learning activities
Learn advanced techniques that bring more accuracy and speed to machine learning
Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow
Book Description
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning - each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
What you will learn
Become familiar with the basics of the TensorFlow machine learning library
Get to know Linear Regression techniques with TensorFlow
Learn SVMs with hands-on recipes
Implement neural networks and improve predictions
Apply NLP and sentiment analysis to your data
Master CNN and RNN through practical recipes
Take TensorFlow into production
Who this book is for
This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful.
Автор: Rivera, Juan De Dios Santos Название: Practical tensorflow.js ISBN: 1484262727 ISBN-13(EAN): 9781484262726 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Do you want to learn how to write your own codes and programming and get your computer set up to learn just like humans do? Do you want to learn how to write out codes in deep learning-without having to spend years going to school to learn to code and how all this works? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This guidebook is the tool that you need to not only learn how to do machine learning but also learn how to take this even further and write some of your own codes in deep learning. The field of deep learning is pretty new, and many programmers have not been able to delve into the depths of what we can see with this type of programming-but with the growing market for products and technology that can act and learn just like the human brain, this field is definitely taking off
This book will take some time to explore the different Python libraries that will help you to do some deep learning algorithms in no time. Investing your time in the Python language and learning the different libraries that are needed to turn this basic programming language into a deep learning machine can be one of the best decisions for you.
By learning some of the tips in this book, you will be able to save time and resources when it comes to your deep learning needs. Rather than spending time with other, more difficult programming languages, or having to go take complicated classes to learn how to do these algorithms, we will explore exactly how to do all of the tasks that you need with this type of machine learning.
You will learn:
1. What deep learning is, how it is different from machine learning, and why Python is such a beneficial language to use with the deep learning algorithms;
2. The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
3. How to install the three Python libraries to help you get started;
4. A closer look at neural networks, what they are, why they are important, and some of the mathematics of making them work;
5. The basics you need to know about TensorFlow and some of the deep learning you can do with this library;
6. The basics of the Keras library and some of the deep learning you can do with this library;
7. A look at the PyTorch library, how it is different from the other two, and the basics of deep learning with this library;
8. And so much more
Even if you are just a beginner, with very little programming knowledge but lots of big dreams and even bigger ideas, this book is going to give you the tools that you need to start with deep learning
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