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• Table of Contents Neural Network Foundations with TensorFlow 2.0 TensorFlow 1.x and 2.x Regression Convolutional Neural Networks Advanced Convolutional Neural Networks Generative Adversarial Networks Word Embeddings Recurrent Neural Networks Autoencoders Unsupervised Learning Reinforcement Learning TensorFlow and Cloud TensorFlow for Mobile and IoT and TensorFlow.js An introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit.
• (source: Nielsen Book Data)

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 DescriptionDeep 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 forThis 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.
(source: Nielsen Book Data)

"直感的かつ短いコードでアイデアを形にでき るKerasはTensorFlowのラッパーとして大人気のライ ブラリです。本書でもTensorFlowをバックエンドと して使用し、自然言語処理、画像識別、画像生 成、音声合成、テキスト生成、強化学習、AIゲ ームプレイなどさまざまなモデルをPythonとKeras で実装します。対象読者は、各種のディープラ ーニングを素早く実装したいプログラマー、デ ータサイエンティスト。ディープラーニングを 支える技術の速習にも好適です。数式はなるべ く使わずにコードと図で説明します。ニューラ ルネットワークおよびPython 3の基本を理解している人であれば誰でも始める ことができます。" -- Provided by publisher. "直感的かつ短いコードでアイデアを形にでき るKerasはTensorFlowのラッパーとして大人気のライ ブラリです。本書でもTensorFlowをバックエンドと して使用し、自然言語処理、画像識別、画像生 成、音声合成、テキスト生成、強化学習、AIゲ ームプレイなどさまざまなモデルをPythonとKeras で実装します。対象読者は、各種のディープラ ーニングを素早く実装したいプログラマー、デ ータサイエンティスト。ディープラーニングを 支える技術の速習にも好適です。数式はなるべ く使わずにコードと図で説明します。ニューラ ルネットワークおよびPython 3の基本を理解している人であれば誰でも始める ことができます。" -- Provided by publisher.

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.
(source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets.

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.