Deep Learning With Tensorflow 2 And Keras

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Deep Learning with TensorFlow 2 and Keras

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1838827722
Total Pages : 647 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Deep Learning with TensorFlow 2 and Keras by : Antonio Gulli

Download or read book Deep Learning with TensorFlow 2 and Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2019-12-27 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Deep Learning Projects Using TensorFlow 2

Author : Vinita Silaparasetty
Publisher : Apress
ISBN 13 : 9781484258019
Total Pages : 421 pages
Book Rating : 4.2/5 (58 download)

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Book Synopsis Deep Learning Projects Using TensorFlow 2 by : Vinita Silaparasetty

Download or read book Deep Learning Projects Using TensorFlow 2 written by Vinita Silaparasetty and published by Apress. This book was released on 2020-08-08 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications. Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts. The best way to learn is by doing. You'll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks and other deep learning concepts. By the end of the book, you'll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application. What You'll Learn Grasp the basic process of neural networks through projects, such as creating music Restore and colorize black and white images with deep learning processes Who This Book Is For Beginners new to TensorFlow and Python.

Advanced Deep Learning with Keras

Author : Rowel Atienza
Publisher : Packt Publishing Ltd
ISBN 13 : 178862453X
Total Pages : 369 pages
Book Rating : 4.7/5 (886 download)

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Book Synopsis Advanced Deep Learning with Keras by : Rowel Atienza

Download or read book Advanced Deep Learning with Keras written by Rowel Atienza and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existence Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning A wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs Book DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learn Cutting-edge techniques in human-like AI performance Implement advanced deep learning models using Keras The building blocks for advanced techniques - MLPs, CNNs, and RNNs Deep neural networks – ResNet and DenseNet Autoencoders and Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep reinforcement learning methods and implementation Produce industry-standard applications using OpenAI Gym Deep Q-Learning and Policy Gradient Methods Who this book is for Some fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.

Hands-on Computer Vision with TensorFlow 2

Author : Benjamin Planche
Publisher :
ISBN 13 : 9781788830645
Total Pages : 372 pages
Book Rating : 4.8/5 (36 download)

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Book Synopsis Hands-on Computer Vision with TensorFlow 2 by : Benjamin Planche

Download or read book Hands-on Computer Vision with TensorFlow 2 written by Benjamin Planche and published by . This book was released on 2019 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be a part of the exciting advances in computer vision.

Deep Learning with TensorFlow 2 and Keras - Second Edition

Author : Antonio Gulli
Publisher :
ISBN 13 : 9781838823412
Total Pages : 646 pages
Book Rating : 4.8/5 (234 download)

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Book Synopsis Deep Learning with TensorFlow 2 and Keras - Second Edition by : Antonio Gulli

Download or read book Deep Learning with TensorFlow 2 and Keras - Second Edition written by Antonio Gulli and published by . This book was released on 2019-12-20 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book 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 learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the most popular approach to machine learning Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers Use GANs (generative adversarial networks) to create new data that fits with existing patterns Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response Train your models on the cloud and put TF to work in real environments Explore how Google tools can automate simple ML workflows without the need for complex modeling Who 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. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems.

Deep Learning with Keras

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1787129039
Total Pages : 310 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Deep Learning with Keras by : Antonio Gulli

Download or read book Deep Learning with Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2017-04-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.

TensorFlow 2 Reinforcement Learning Cookbook

Author : Praveen Palanisamy
Publisher : Packt Publishing Ltd
ISBN 13 : 1838985999
Total Pages : 473 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis TensorFlow 2 Reinforcement Learning Cookbook by : Praveen Palanisamy

Download or read book TensorFlow 2 Reinforcement Learning Cookbook written by Praveen Palanisamy and published by Packt Publishing Ltd. This book was released on 2021-01-15 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook Description With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch. What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is for The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

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

Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149203259X
Total Pages : 851 pages
Book Rating : 4.4/5 (92 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 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Applied Neural Networks with TensorFlow 2

Author : Orhan Gazi Yalçın
Publisher : Apress
ISBN 13 : 9781484265123
Total Pages : 295 pages
Book Rating : 4.2/5 (651 download)

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Book Synopsis Applied Neural Networks with TensorFlow 2 by : Orhan Gazi Yalçın

Download or read book Applied Neural Networks with TensorFlow 2 written by Orhan Gazi Yalçın and published by Apress. This book was released on 2020-11-30 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy—others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. What You'll Learn Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks Who This Book Is For Data scientists and programmers new to the fields of deep learning and machine learning APIs.

Deep Learning with Python

Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with Python by : Francois Chollet

Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Deep Learning with TensorFlow and Keras

Author : Amita Kapoor
Publisher : Packt Publishing Ltd
ISBN 13 : 1803245719
Total Pages : 699 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Deep Learning with TensorFlow and Keras by : Amita Kapoor

Download or read book Deep Learning with TensorFlow and Keras written by Amita Kapoor and published by Packt Publishing Ltd. This book was released on 2022-10-06 with total page 699 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Python Deep Learning

Author : Ivan Vasilev
Publisher : Packt Publishing Ltd
ISBN 13 : 1789349702
Total Pages : 379 pages
Book Rating : 4.7/5 (893 download)

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Book Synopsis Python Deep Learning by : Ivan Vasilev

Download or read book Python Deep Learning written by Ivan Vasilev and published by Packt Publishing Ltd. This book was released on 2019-01-16 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

TensorFlow for Deep Learning

Author : Bharath Ramsundar
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491980400
Total Pages : 256 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis TensorFlow for Deep Learning by : Bharath Ramsundar

Download or read book TensorFlow for Deep Learning written by Bharath Ramsundar and published by "O'Reilly Media, Inc.". This book was released on 2018-03-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units

Learning TensorFlow

Author : Tom Hope
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491978481
Total Pages : 242 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Learning TensorFlow by : Tom Hope

Download or read book Learning TensorFlow written by Tom Hope and published by "O'Reilly Media, Inc.". This book was released on 2017-08-09 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

TensorFlow in Action

Author : Thushan Ganegedara
Publisher : Simon and Schuster
ISBN 13 : 1638356734
Total Pages : 678 pages
Book Rating : 4.6/5 (383 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 2022-11-01 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. 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

What's New in TensorFlow 2.0

Author : Ajay Baranwal
Publisher : Packt Publishing Ltd
ISBN 13 : 1838828834
Total Pages : 192 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis What's New in TensorFlow 2.0 by : Ajay Baranwal

Download or read book What's New in TensorFlow 2.0 written by Ajay Baranwal and published by Packt Publishing Ltd. This book was released on 2019-08-12 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.