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: Gulli Antonio, Pal Sujit, Kapoor Amita Название: Deep Learning with TensorFlow 2 and Keras - Second Edition ISBN: 1838823417 ISBN-13(EAN): 9781838823412 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: ITIL(R) 4 Managing Professional CoursewareITIL 4 Managing Professional (ITIL MP) consists of four modules and is the next level of ITIL 4 to be released after ITIL 4 Foundation. ITIL MP targets IT practitioners working within technology and digital teams across businesses. The Managing Professional (MP) stream provides practical and technical knowledge about how to run successful IT-enabled services, teams and workflows. The Managing Professional Transition module is designed to allow ITIL v3 candidates to easily transition to ITIL 4. They can get the ITIL 4 Managing Professional designation through one course and one exam. The material includes;¢ Updated glossaries with highlighted changes for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ New diagram packs with annotations for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ Updated syllabi with highlighted changes for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ The `High Velocity IT` manuscript, syllabus, glossary and diagram pack remain unchanged¢ Practices Overviews and the product brochure remain unchanged¢ An up-to-date Quick Reference Guide with all the information you need¢ Updated Core Manuscripts, for `Create, Deliver & Support`, `Drive Stakeholder Value` and `Direct, Plan & Improve`¢ While the changes are quite large, they have been made for streamlining or refinement only¢ There have been no changes to the examinable content or the key concepts
Автор: Boschetti Alberto, Massaron Luca, Thakur Abhishek Название: Tensorflow Deep Learning Projects ISBN: 1788398068 ISBN-13(EAN): 9781788398060 Издательство: Неизвестно Цена: 8091.00 р. 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.
Автор: Koonce, Brett Название: Convolutional neural networks with swift for tensorflow ISBN: 1484261674 ISBN-13(EAN): 9781484261675 Издательство: Springer Рейтинг: Цена: 7685.00 р. 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.
Автор: Galeone, Paolo Название: Hands-on neural networks with tensorflow 2.0 ISBN: 1789615550 ISBN-13(EAN): 9781789615555 Издательство: Неизвестно Рейтинг: Цена: 7363.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a guide to the TensorFlow (TF) framework, from the static graph architecture of TF 1.x to the eager execution and all the new features introduced in TF 2.0. Neural Networks applications are developed throughout the book with the aim of making the reader capable of developing neural networks-based solutions to real problems using TF 2.0
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.
Описание: Deep Learning is the next big thing. It is a part of machine learning. Its favorable results in application with huge and complex data is remarkable. This book will help you to get through the problems that you face during the execution of different tasks and understand hacks in deep learning, neural networks, and advanced machine learning techn...
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: Zaccone Giancarlo, Karim MD Rezaul Название: Deep Learning with Tensorflow - Second Edition ISBN: 1788831101 ISBN-13(EAN): 9781788831109 Издательство: Неизвестно Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection.
Автор: Gulli Antonio, Kapoor Amita Название: Tensorflow 1.X Deep Learning Cookbook ISBN: 1788293592 ISBN-13(EAN): 9781788293594 Издательство: Неизвестно Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book - Skill up and implement tricky neural networks using Google's TensorFlow 1.x - An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. - Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn - Install TensorFlow and use it for CPU and GPU operations - Implement DNNs and apply them to solve different AI-driven problems. - Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. - Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. - Use different regression techniques for prediction and classification problems - Build single and multilayer perceptrons in TensorFlow - Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. - Learn how restricted Boltzmann Machines can be used to recommend movies. - Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. - Master the different reinforcement learning methods to implement game playing agents. - GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
Автор: Hope Tom, Resheff Yehezkel S., Lieder Itay Название: Learning Tensorflow: A Guide to Building Deep Learning Systems ISBN: 1491978511 ISBN-13(EAN): 9781491978511 Издательство: Wiley Рейтинг: Цена: 7602.00 р. 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 computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.
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