Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guideKey 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[•] Real-world contextualization through some deep learning problems concerning research and applicationBook DescriptionDeep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.What you will learn[•]Learn about machine learning landscapes along with the historical development and progress of deep learning[•]Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x[•] Access public datasets and utilize them using TensorFlow to load, process, and transform data[•] Use TensorFlow on real-world datasets, including images, text, and more[•] Learn how to evaluate the performance of your deep learning models[•] Using deep learning for scalable object detection and mobile computing[•]Train machines quickly to learn from data by exploring reinforcementlearning techniques[•]Explore active areas of deep learning research and applicationsWho this book is forThe book is intended for a general audience of 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.

lgli/9781788399906.epub

lgrsnf/9781788399906.epub

zlib/Computers/Programming/Ahmed Menshawy/Deep Learning By Example_3600732.epub

Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow 1.x

Deep Learning with Keras : Get to Grips with the Basics of Keras to Implement Fast and Efficient Deep-learning Models

Библиотека Keras - инструмент глубокого обучения: реализация нейронных сетей с помощью библиотек Theano и TensorFlow

Deep learning with Keras : implementing deep learning models and neural networks with the power of Python

Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow

Deep Learning with TensorFlow : Explore Neural Networks with Python

Zaccone, Giancarlo, Karim, Md. Rezaul, Menshawy, Ahmed

Антонио Джулли, Суджит Пал; пер. с англ. Слинкин А. А

Gulli, Antonio, Pal, Sujit

Antonio Gulli; Sujit Pal

Джулли, Антонио

Packt Publishing Limited

Packt; Packt Publishing

ДМК Пресс

1st ed, Erscheinungsort nicht ermittelbar, 2018

United Kingdom and Ireland, United Kingdom

Packt Publishing, Birmingham, UK, 2017

Москва, Russia, 2018

Feb 28, 2018

Apr 24, 2017

2018-02-28

0

lg2269796

{"publisher":"Packt Publishing"}

Предм. указ.: с. 290-293
Ориг.: Gulli, Antonio Deep learning with Keras 978-1-78712-842-2

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"Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book * 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 * Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of 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. What You Will Learn * Learn about machine learning landscapes along with the historical development and progress of deep learning * Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x * Access public datasets and utilize them using TensorFlow to load, process, and transform data * Use TensorFlow on real-world datasets, including images, text, and more * Learn how to evaluate the performance of your deep learning models * Using 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 In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you`ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you`ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing" -- Dawsonera

Annotation Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on mannerKey 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 examplesBook DescriptionDeep 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 learnUnderstand the fundamentals of deep learning and how it is different from machine learningGet familiarized with Tensorflow, one of the most popular libraries for advanced machine learningIncrease the predictive power of your model using feature engineeringUnderstand the basics of deep learning by solving a digit classification problem of MNISTDemonstrate face generation based on the CelebA database, a promising application of generative modelsApply deep learning to other domains like language modeling, sentiment analysis, and machine translationWho this book is forThis 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

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..

"Deep Learning is a subset of Machine Learning and has gained a lot of popularity recently. This book introduces you to the fundamentals of deep learning in a hands-on manner. You will use Tensorflow to train different types of neural networks for tasks related to computer vision, language processing, and other real-world problems."--Publisher's description

2018-10-01