Deep Learning With Tensorflow
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Learn Tensorflow 2 0
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Author : Pramod Singh
language : en
Publisher: Apress
Release Date : 2019-12-17
Learn Tensorflow 2 0 written by Pramod Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with Computers categories.
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
Learning Tensorflow
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Author : Tom Hope
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-09
Learning Tensorflow written by Tom Hope 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 2017-08-09 with Computers categories.
Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. 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. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting
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.
Advanced Deep Learning With Tensorflow 2 And Keras Second Edition
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Author : ROWEL. ATIENZA
language : en
Publisher:
Release Date : 2020-02-28
Advanced Deep Learning With Tensorflow 2 And Keras Second Edition written by ROWEL. ATIENZA and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with categories.
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.
Hands On Computer Vision With Tensorflow 2
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Author : Benjamin Planche
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-30
Hands On Computer Vision With Tensorflow 2 written by Benjamin Planche 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 2019-05-30 with Computers categories.
A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more Key FeaturesDiscover how to build, train, and serve your own deep neural networks with TensorFlow 2 and KerasApply modern solutions to a wide range of applications such as object detection and video analysisLearn how to run your models on mobile devices and web pages and improve their performanceBook Description Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0. What you will learnCreate your own neural networks from scratchClassify images with modern architectures including Inception and ResNetDetect and segment objects in images with YOLO, Mask R-CNN, and U-NetTackle problems faced when developing self-driving cars and facial emotion recognition systemsBoost your application's performance with transfer learning, GANs, and domain adaptationUse recurrent neural networks (RNNs) for video analysisOptimize and deploy your networks on mobile devices and in the browserWho this book is for If you're new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you're an expert curious about the new TensorFlow 2 features, you'll find this book useful. While some theoretical concepts require knowledge of algebra and calculus, the book covers concrete examples focused on practical applications such as visual recognition for self-driving cars and smartphone apps.
Deep Learning For Beginners With Tensorflow
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Author : Mark Smart
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-09-13
Deep Learning For Beginners With Tensorflow written by Mark Smart and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-13 with categories.
This book is an exploration of deep learning in Python using TensorFlow. The author guides you on how to create machine learning models using TensorFlow. You will know the initial steps of getting started with TensorFlow in Python. This involves installing TensorFlow and writing your first code. TensorFlow works using the concept of graphs. The author helps you know how expressions are represented into graphs in TensorFlow. Deep learning in Python with TensorFlow simply involves the creation of neural network models. The author helps you understand how to create neural network models with TensorFlow. You are guided on how to train such models with data of various types. Examples of such data include images and text. The process of loading your own data into TensorFlow for training neural network models has also been discussed. You will also know how to use the inbuilt data for training your neural network models. You will learn from this book: Getting started Building a Neural Network Working with Images Importing Data Subjects include: tensorflow python, deep learning with python, tensorflow machine learning, tensor flow, tensorflow deep learning cookbook, tensorflow for deep learning, tensorflow for dummies, tensorflow books, machine learning with tensorflow, tensorflow c++, concept of graphs, neural network, neural networks python, tensorflow with neural network.
Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar 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 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Tinyml
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Author : Pete Warden
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-12-16
Tinyml written by Pete Warden 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 2019-12-16 with Computers categories.
Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Pro Deep Learning With Tensorflow
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Author : Santanu Pattanayak
language : en
Publisher: Apress
Release Date : 2017-12-06
Pro Deep Learning With Tensorflow written by Santanu Pattanayak and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-06 with Computers categories.
Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. What You'll Learn Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow Who This Book Is For Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts