Tensorflow In Action

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TensorFlow in Action

Author : Thushan Ganegedara
Publisher : Simon and Schuster
ISBN 13 : 1617298344
Total Pages : 678 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis TensorFlow in Action by : Thushan Ganegedara

Download or read book TensorFlow in Action written by Thushan Ganegedara and published by Simon and Schuster. This book was released on 2022-10-18 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the TensorFlow design secrets behind successful deep learning applications! Deep learning StackOverflow contributor Thushan Ganegedara teaches you the new features of TensorFlow 2 in this hands-on guide. In TensorFlow in Action you will learn: Fundamentals of TensorFlow Implementing deep learning networks Picking a high-level Keras API for model building with confidence Writing comprehensive end-to-end data pipelines Building models for computer vision and natural language processing Utilizing pretrained NLP models Recent algorithms including transformers, attention models, and ElMo In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. Author Thushan Ganegedara uses quirky stories, practical examples, and behind-the-scenes explanations to demystify concepts otherwise trapped in dense academic papers. As you dive into modern deep learning techniques like transformer and attention models, you’ll benefit from the unique insights of a top StackOverflow contributor for deep learning and NLP. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Google’s TensorFlow framework sits at the heart of modern deep learning. Boasting practical features like multi-GPU support, network data visualization, and easy production pipelines using TensorFlow Extended (TFX), TensorFlow provides the most efficient path to professional AI applications. And the Keras library, fully integrated into TensorFlow 2, makes it a snap to build and train even complex models for vision, language, and more. About the book TensorFlow in Action teaches you to construct, train, and deploy deep learning models using TensorFlow 2. In this practical tutorial, you’ll build reusable skill hands-on as you create production-ready applications such as a French-to-English translator and a neural network that can write fiction. You’ll appreciate the in-depth explanations that go from DL basics to advanced applications in NLP, image processing, and MLOps, complete with important details that you’ll return to reference over and over. What's inside Covers TensorFlow 2.9 Recent algorithms including transformers, attention models, and ElMo Build on pretrained models Writing end-to-end data pipelines with TFX About the reader For Python programmers with basic deep learning skills. About the author Thushan Ganegedara is a senior ML engineer at Canva and TensorFlow expert. He holds a PhD in machine learning from the University of Sydney. Table of Contents PART 1 FOUNDATIONS OF TENSORFLOW 2 AND DEEP LEARNING 1 The amazing world of TensorFlow 2 TensorFlow 2 3 Keras and data retrieval in TensorFlow 2 4 Dipping toes in deep learning 5 State-of-the-art in deep learning: Transformers PART 2 LOOK MA, NO HANDS! DEEP NETWORKS IN THE REAL WORLD 6 Teaching machines to see: Image classification with CNNs 7 Teaching machines to see better: Improving CNNs and making them confess 8 Telling things apart: Image segmentation 9 Natural language processing with TensorFlow: Sentiment analysis 10 Natural language processing with TensorFlow: Language modeling PART 3 ADVANCED DEEP NETWORKS FOR COMPLEX PROBLEMS 11 Sequence-to-sequence learning: Part 1 12 Sequence-to-sequence learning: Part 2 13 Transformers 14 TensorBoard: Big brother of TensorFlow 15 TFX: MLOps and deploying models with TensorFlow

Deep Learning Essentials

Author : Anurag Bhardwaj
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887777
Total Pages : 271 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Deep Learning Essentials by : Anurag Bhardwaj

Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Mastering TensorFlow 1.x

Author : Armando Fandango
Publisher : Packt Publishing Ltd
ISBN 13 : 1788297008
Total Pages : 464 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Mastering TensorFlow 1.x by : Armando Fandango

Download or read book Mastering TensorFlow 1.x written by Armando Fandango and published by Packt Publishing Ltd. This book was released on 2018-01-22 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

TensorFlow For Dummies

Author : Matthew Scarpino
Publisher : John Wiley & Sons
ISBN 13 : 1119466210
Total Pages : 368 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis TensorFlow For Dummies by : Matthew Scarpino

Download or read book TensorFlow For Dummies written by Matthew Scarpino and published by John Wiley & Sons. This book was released on 2018-04-03 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool! Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. Install TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with TensorBoard Perform image recognition with convolutional neural networks (CNNs) Analyze sequential data with recurrent neural networks (RNNs) Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP) If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.

TensorFlow Reinforcement Learning Quick Start Guide

Author : Kaushik Balakrishnan
Publisher : Packt Publishing Ltd
ISBN 13 : 1789533449
Total Pages : 175 pages
Book Rating : 4.7/5 (895 download)

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Book Synopsis TensorFlow Reinforcement Learning Quick Start Guide by : Kaushik Balakrishnan

Download or read book TensorFlow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan and published by Packt Publishing Ltd. This book was released on 2019-03-30 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109812247X
Total Pages : 863 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by : Aurélien Géron

Download or read book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2022-10-04 with total page 863 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a recent series of 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. This bestselling book uses concrete examples, minimal theory, and 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. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

Machine Learning with TensorFlow, Second Edition

Author : Mattmann A. Chris
Publisher : Manning Publications
ISBN 13 : 1617297712
Total Pages : 454 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Machine Learning with TensorFlow, Second Edition by : Mattmann A. Chris

Download or read book Machine Learning with TensorFlow, Second Edition written by Mattmann A. Chris and published by Manning Publications. This book was released on 2021-02-02 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

TensorFlow Machine Learning Projects

Author : Ankit Jain
Publisher : Packt Publishing Ltd
ISBN 13 : 1789132401
Total Pages : 311 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis TensorFlow Machine Learning Projects by : Ankit Jain

Download or read book TensorFlow Machine Learning Projects written by Ankit Jain and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow's impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook Description TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work. What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques

Advanced Deep Learning with TensorFlow 2 and Keras

Author : Rowel Atienza
Publisher : Packt Publishing Ltd
ISBN 13 : 183882572X
Total Pages : 513 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Advanced Deep Learning with TensorFlow 2 and Keras by : Rowel Atienza

Download or read book Advanced Deep Learning with TensorFlow 2 and Keras written by Rowel Atienza and published by Packt Publishing Ltd. This book was released on 2020-02-28 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models – autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.

Applied Deep Learning with TensorFlow 2

Author : Umberto Michelucci
Publisher : Apress
ISBN 13 : 9781484280195
Total Pages : 380 pages
Book Rating : 4.2/5 (81 download)

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Book Synopsis Applied Deep Learning with TensorFlow 2 by : Umberto Michelucci

Download or read book Applied Deep Learning with TensorFlow 2 written by Umberto Michelucci and published by Apress. This book was released on 2022-04-18 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: • Understand the fundamental concepts of how neural networks work • Learn the fundamental ideas behind autoencoders and generative adversarial networks • Be able to try all the examples with complete code examples that you can expand for your own projects • Have available a complete online companion book with examples and tutorials. This book is for: Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.

Deep Learning By Example

Author : Ahmed Menshawy
Publisher : Packt Publishing Ltd
ISBN 13 : 178839576X
Total Pages : 442 pages
Book Rating : 4.7/5 (883 download)

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Book Synopsis Deep Learning By Example by : Ahmed Menshawy

Download or read book Deep Learning By Example written by Ahmed Menshawy and published by Packt Publishing Ltd. This book was released on 2018-02-28 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner Key Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples Book Description Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learn Understand the fundamentals of deep learning and how it is different from machine learning Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning Increase the predictive power of your model using feature engineering Understand the basics of deep learning by solving a digit classification problem of MNIST Demonstrate face generation based on the CelebA database, a promising application of generative models Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation Who this book is for This book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.

Reinforcement Learning with TensorFlow

Author : Sayon Dutta
Publisher : Packt Publishing Ltd
ISBN 13 : 1788830717
Total Pages : 327 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Reinforcement Learning with TensorFlow by : Sayon Dutta

Download or read book Reinforcement Learning with TensorFlow written by Sayon Dutta and published by Packt Publishing Ltd. This book was released on 2018-04-24 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.

Hands-On Reinforcement Learning with Python

Author : Sudharsan Ravichandiran
Publisher : Packt Publishing Ltd
ISBN 13 : 178883691X
Total Pages : 309 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Hands-On Reinforcement Learning with Python by : Sudharsan Ravichandiran

Download or read book Hands-On Reinforcement Learning with Python written by Sudharsan Ravichandiran and published by Packt Publishing Ltd. This book was released on 2018-06-28 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. What you will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman’s optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN Who this book is for If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

Mastering Computer Vision with TensorFlow 2.x

Author : Krishnendu Kar
Publisher : Packt Publishing Ltd
ISBN 13 : 1838826939
Total Pages : 419 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Mastering Computer Vision with TensorFlow 2.x by : Krishnendu Kar

Download or read book Mastering Computer Vision with TensorFlow 2.x written by Krishnendu Kar and published by Packt Publishing Ltd. This book was released on 2020-05-15 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learnExplore methods of feature extraction and image retrieval and visualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-world scenariosBuild neural networks or adjust parameters to optimize the performance of modelsUnderstand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpaintingEvaluate your model and optimize and integrate it into your application to operate at scaleGet up to speed with techniques for performing manual and automated image annotationWho this book is for This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

TensorFlow 1.x Deep Learning Cookbook

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1788291867
Total Pages : 526 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis TensorFlow 1.x Deep Learning Cookbook by : Antonio Gulli

Download or read book TensorFlow 1.x Deep Learning Cookbook written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2017-12-12 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python Reinforcement Learning Projects

Author : Sean Saito
Publisher : Packt Publishing Ltd
ISBN 13 : 1788993225
Total Pages : 287 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Python Reinforcement Learning Projects by : Sean Saito

Download or read book Python Reinforcement Learning Projects written by Sean Saito and published by Packt Publishing Ltd. This book was released on 2018-09-29 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key FeaturesImplement Q-learning and Markov models with Python and OpenAIExplore the power of TensorFlow to build self-learning modelsEight AI projects to gain confidence in building self-trained applicationsBook Description Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life. What you will learnTrain and evaluate neural networks built using TensorFlow for RLUse RL algorithms in Python and TensorFlow to solve CartPole balancingCreate deep reinforcement learning algorithms to play Atari gamesDeploy RL algorithms using OpenAI UniverseDevelop an agent to chat with humans Implement basic actor-critic algorithms for continuous controlApply advanced deep RL algorithms to games such as MinecraftAutogenerate an image classifier using RLWho this book is for Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.

Deep Learning with TensorFlow

Author : Giancarlo Zaccone
Publisher : Packt Publishing Ltd
ISBN 13 : 1788831837
Total Pages : 483 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Deep Learning with TensorFlow by : Giancarlo Zaccone

Download or read book Deep Learning with TensorFlow written by Giancarlo Zaccone and published by Packt Publishing Ltd. This book was released on 2018-03-30 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features 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 Gain real-world contextualization through some deep learning problems concerning research and application Book Description Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use 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 applications Who this book is for The book is for 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.