Machine Learning With Tensorflow 1 X
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Machine Learning With Tensorflow 1 X
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Author : Quan Hua
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-21
Machine Learning With Tensorflow 1 X written by Quan Hua 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 2017-11-21 with Computers categories.
Tackle common commercial machine learning problems with Google's TensorFlow 1.x library and build deployable solutions. About This Book Enter the new era of second-generation machine learning with Python with this practical and insightful guide Set up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU support Create pipelines for training and using applying classifiers using raw real-world data Who This Book Is For This book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow's unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build deep neural networks using TensorFlow 1.x Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Learn how to use multiple GPUs for faster training using AWS In Detail Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you'll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data flow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You'll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you'll implement a complete real-life production system from training to serving a deep learning model. As you advance you'll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you'll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim. By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment. Style and approach This comprehensive guide will enable you to understand the latest advances in machine learning and will empower you to implement this knowledge in your machine learning environment.
Mastering Tensorflow 1 X
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Author : Armando Fandango
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-22
Mastering Tensorflow 1 X written by Armando Fandango 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 2018-01-22 with Computers categories.
Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.
Machine Learning With Tensorflow
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Author : Saif Ahmed
language : en
Publisher:
Release Date : 2016-12-30
Machine Learning With Tensorflow written by Saif Ahmed and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-30 with categories.
Tackle common commercial machine learning problems with Google's TensorFlow library and build deployable solutions.About This Book* Set up TensorFlow for actual industrial use, including high-performance setup aspects such as multi-GPU support* Create pipelines for training and using applying classifiers using raw real-world data* Productionize challenges and deploy solutions into a production settingWho This Book Is ForThis book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow's unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected.What You Will Learn* Set up basic and advanced TensorFlow installations* Deep-dive into training, validating, and monitoring training performance* Set up and run cross-sectional examples (images, time-series, text, and audio)* Create pipelines to deal with real-world input data* Set up and run cross domain-specific examples (economics, medicine, text classification, and advertising)* Empower the reader to go from concept to a production-ready machine learning setup/pipeline capable of real-world usageIn DetailTensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.This book approaches common commercial machine learning problems using Google's TensorFlow library. It will cover unique features of the library such as Data Flow Graphs, training, visualisation of performance with TensorBoard-all within an example-rich context using problems from multiple industries. The is on introducing new concepts through problems that are coded and solved over the course of each chapter.
Tensorflow 1 X Deep Learning Cookbook
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Author : Antonio Gulli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-12-12
Tensorflow 1 X Deep Learning Cookbook written by Antonio Gulli 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 2017-12-12 with Computers categories.
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.
Mastering Tensorflow 2 X
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Author : Rajdeep
language : en
Publisher: BPB Publications
Release Date : 2022-03-24
Mastering Tensorflow 2 X written by Rajdeep and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-24 with Antiques & Collectibles categories.
Work with TensorFlow and Keras for real performance of deep learning KEY FEATURES ● Combines theory and implementation with in-detail use-cases. ● Coverage on both, TensorFlow 1.x and 2.x with elaborated concepts. ● Exposure to Distributed Training, GANs and Reinforcement Learning. DESCRIPTION Mastering TensorFlow 2.x is a must to read and practice if you are interested in building various kinds of neural networks with high level TensorFlow and Keras APIs. The book begins with the basics of TensorFlow and neural network concepts, and goes into specific topics like image classification, object detection, time series forecasting and Generative Adversarial Networks. While we are practicing TensorFlow 2.6 in this book, the version of Tensorflow will change with time; however you can still use this book to witness how Tensorflow outperforms. This book includes the use of a local Jupyter notebook and the use of Google Colab in various use cases including GAN and Image classification tasks. While you explore the performance of TensorFlow, the book also covers various concepts and in-detail explanations around reinforcement learning, model optimization and time series models. WHAT YOU WILL LEARN ● Getting started with Tensorflow 2.x and basic building blocks. ● Get well versed in functional programming with TensorFlow. ● Practice Time Series analysis along with strong understanding of concepts. ● Get introduced to use of TensorFlow in Reinforcement learning and Generative Adversarial Networks. ● Train distributed models and how to optimize them. WHO THIS BOOK IS FOR This book is designed for machine learning engineers, NLP engineers and deep learning practitioners who want to utilize the performance of TensorFlow in their ML and AI projects. Readers are expected to have some familiarity with Tensorflow and the basics of machine learning would be helpful. TABLE OF CONTENTS 1. Getting started with TensorFlow 2.x 2. Machine Learning with TensorFlow 2.x 3. Keras based APIs 4. Convolutional Neural Networks in Tensorflow 5. Text Processing with TensorFlow 2.x 6. Time Series Forecasting with TensorFlow 2.x 7. Distributed Training and DataInput pipelines 8. Reinforcement Learning 9. Model Optimization 10. Generative Adversarial Networks
Deep Learning With Tensorflow
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Author : Giancarlo Zaccone
language : en
Publisher:
Release Date : 2017-04-24
Deep Learning With Tensorflow written by Giancarlo Zaccone and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-24 with Computers categories.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guideAbout This Book* Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow* Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide* Real-world contextualization through some deep learning problems concerning research and application Who This Book Is ForThe book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.What You Will Learn* Learn about machine learning landscapes along with the historical development and progress of deep learning* Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x* Access public datasets and utilize them using TensorFlow to load, process, and transform data* Use TensorFlow on real-world datasets, including images, text, and more* Learn how to evaluate the performance of your deep learning models* Using deep learning for scalable object detection and mobile computing* Train machines quickly to learn from data by exploring reinforcement learning techniques* Explore active areas of deep learning research and applicationsIn DetailDeep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x.Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.Style and approachThis step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
Beginning With Deep Learning Using Tensorflow
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Author : Mohan Kumar Silaparasetty
language : en
Publisher: BPB Publications
Release Date : 2022-02-09
Beginning With Deep Learning Using Tensorflow written by Mohan Kumar Silaparasetty and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-09 with Antiques & Collectibles categories.
A Practicing Guide to TensorFlow and Deep Learning KEY FEATURES ● Equipped with a necessary introduction to Deep Learning and AI. ● Includes demos and templates to give your projects a good start. ● Find more on the most important facets of AI, image, and speech recognition. DESCRIPTION This book begins with the configuration of an Anaconda development environment, essential for practicing the deep learning process. The basics of machine learning, which are needed for Deep Learning, are explained in this book. TensorFlow is the industry-standard library for Deep Learning, and thereby, it is covered extensively with both versions, 1.x and 2.x. As neural networks are the heart of Deep Learning, the book explains them in great detail and systematic fashion, beginning with a single neuron and progressing through deep multilayer neural networks. The emphasis of this book is on the practical application of key concepts rather than going in-depth with them. After establishing a firm basis in TensorFlow and Neural Networks, the book explains the concepts of image recognition using Convolutional Neural Networks (CNN), followed by speech recognition, and natural language processing (NLP). Additionally, this book discusses Transformers, the most recent advancement in NLP. WHAT YOU WILL LEARN ● Create machine learning models for classification and regression. ● Utilize TensorFlow 1.x to implement neural networks. ● Work with the Keras API and TensorFlow 2. ● Learn to design and train image categorization models. ● Construct translation and Q & A apps using transformer-based language models. WHO THIS BOOK IS FOR This book is intended for those new to Deep Learning who want to learn about its principles and applications. Before you begin, you'll need to be familiar with Python. TABLE OF CONTENTS 1. Introduction to Artificial Intelligence 2. Machine Learning 3. TensorFlow Programming 4. Neural Networks 5. TensorFlow 2 6. Image Recognition 7. Speech Recognition
Deep Learning With Tensorflow 2 And Keras
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Author : Antonio Gulli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-27
Deep Learning With Tensorflow 2 And Keras written by Antonio Gulli 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 2019-12-27 with Computers categories.
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is for This 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 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.
Programming With Tensorflow
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Author : Kolla Bhanu Prakash
language : en
Publisher: Springer Nature
Release Date : 2021-01-22
Programming With Tensorflow written by Kolla Bhanu Prakash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-22 with Technology & Engineering categories.
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. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).
What S New In Tensorflow 2 0
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Author : Ajay Baranwal
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-12
What S New In Tensorflow 2 0 written by Ajay Baranwal 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 2019-08-12 with Computers categories.
Get to grips with key structural changes in TensorFlow 2.0 Key FeaturesExplore TF Keras APIs and strategies to run GPUs, TPUs, and compatible APIs across the TensorFlow ecosystemLearn and implement best practices for building data ingestion pipelines using TF 2.0 APIsMigrate your existing code from TensorFlow 1.x to TensorFlow 2.0 seamlesslyBook Description TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This book will help you understand and utilize the latest TensorFlow features. What's New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The book then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. You'll explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This book delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis. By the end of the book, you'll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly. What you will learnImplement tf.keras APIs in TF 2.0 to build, train, and deploy production-grade modelsBuild models with Keras integration and eager executionExplore distribution strategies to run models on GPUs and TPUsPerform what-if analysis with TensorBoard across a variety of modelsDiscover Vision Kit, Voice Kit, and the Edge TPU for model deploymentsBuild complex input data pipelines for ingesting large training datasetsWho this book is for If you’re a data scientist, machine learning practitioner, deep learning researcher, or AI enthusiast who wants to migrate code to TensorFlow 2.0 and explore the latest features of TensorFlow 2.0, this book is for you. Prior experience with TensorFlow and Python programming is necessary to understand the concepts covered in the book.