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Deep Learning Quick Reference : Useful Hacks for Training and Optimizing Deep Neural Networks with TensorFlow and Keras 🔍
Michael Bernico Packt Publishing - ebooks Account, Packt Publishing, Birmingham, 2018
英语 [en] · MOBI · 14.2MB · 2018 · 📘 非小说类图书 · 🚀/lgli/lgrs/upload · Save
描述
This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Packed with useful hacks to solve real-world challenges along with the supported math and theory around each topic, this book will be a quick reference for training and optimize your deep neural networks.
COM004000 - COMPUTERS / Intelligence (AI) and Semantics,COM018000 - COMPUTERS / Data Processing,COM037000 - COMPUTERS / Machine Theory
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lgli/fKCS8xxoyyjUgGtsLhjfZS.azw
备用文件名
lgrsnf/fKCS8xxoyyjUgGtsLhjfZS.azw
备选标题
TensorFlow 1.x Deep Learning Cookbook : Take the Next Step in Implementing Various Common and Not-so-common Neural Networks with Tensorflow 1.x
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TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python
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Deep Learning with Keras : Get to Grips with the Basics of Keras to Implement Fast and Efficient Deep-learning Models
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Библиотека Keras - инструмент глубокого обучения: реализация нейронных сетей с помощью библиотек Theano и TensorFlow
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Deep learning with Keras : implementing deep learning models and neural networks with the power of Python
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Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
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TensorFlow 1. X Deep Learning Cookbook
备选作者
Антонио Джулли, Суджит Пал; пер. с англ. Слинкин А. А
备选作者
Gulli, Antonio, Kapoor, Amita
备选作者
Antonio Gulli; Amita Kapoor
备选作者
Gulli, Antonio, Pal, Sujit
备选作者
Antonio Gulli; Sujit Pal
备选作者
Джулли, Антонио
备选作者
Antonio Gullì
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Bernico, Mike
备选作者
Mike Bernico
备用出版商
Packt Publishing Limited
备用出版商
Packt; Packt Publishing
备用出版商
ДМК Пресс
备用版本
United Kingdom and Ireland, United Kingdom
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Place of publication not identified, 2018
备用版本
Packt Publishing, Birmingham, UK, 2017
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1st ed, Birmingham, 2018
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Birmingham, UK, 2018
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Москва, Russia, 2018
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Dec 12, 2017
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Mar 09, 2018
备用版本
2017-12-12
备用扩展名
azw
元数据中的注释
Предм. указ.: с. 290-293
Ориг.: Gulli, Antonio Deep learning with Keras 978-1-78712-842-2
元数据中的注释
РГБ
元数据中的注释
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备用描述
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.xAbout This BookSkill up and implement tricky neural networks using Google's TensorFlow 1.xAn 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 environmentWho This Book Is ForThis 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 LearnInstall TensorFlow and use it for CPU and GPU operationsImplement 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 problemsBuild single and multilayer perceptrons in TensorFlowImplement 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 DetailDeep 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 approachThis 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.
备用描述
Dive deeper into neural networks and get your models trained, optimized with this quick reference guideKey Features[•]A quick reference to all important deep learning concepts and their implementations[•]Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more[•]Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow.Book DescriptionDeep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.What you will learn[•] Solve regression and classification challenges with TensorFlow and Keras[•] Learn to use Tensor Board for monitoring neural networks and its training[•] Optimize hyperparameters and safe choices/best practices[•] Build CNN's, RNN's, and LSTM's and using word embedding from scratch[•] Build and train seq2seq models for machine translation and chat applications.[•] Understanding Deep Q networks and how to use one to solve an autonomous agent problem.[•] Explore Deep Q Network and address autonomous agent challenges.Who this book is forIf you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.
备用描述
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-learnin ..
备用描述
Dive deeper into neural networks and get your models trained, optimized with this quick reference guide About This Book A quick reference to all important deep learning concepts and their implementations Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Who This Book Is For If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required. What You Will Learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's and using word embedding from scratch Build and train seq2seq models for machine translation and chat applications. Understanding Deep Q networks and how to use one to solve an autonomous agent problem. Explore Deep Q Network and address autonomous agent challenges. In Detail Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best pr ..
备用描述
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. Downloading the example code for thi..
备用描述
"Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book, deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later, the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks"--Page 4 of cover
开源日期
2024-12-16
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