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 ...

lgli/Z:\Bibliotik_\19\D\Deep_Learning_with_Keras.mobi

lgrsnf/Z:\Bibliotik_\19\D\Deep_Learning_with_Keras.mobi

nexusstc/Deep Learning with Keras: Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games/8f439d04e8922530f543f7c07bb670ea.mobi

zlib/Computers/Computer Science/Antonio Gulli, Sujit Pal/Deep Learning with Keras_5819085.mobi

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

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

Antonio Gulli, Sujit Pal

Джулли, Антонио

Packt Publishing Limited

ДМК Пресс

United Kingdom and Ireland, United Kingdom

Packt Publishing, Birmingham, UK, 2017

Москва, Russia, 2018

lg2631755

{"edition":"1","isbns":["1787128423","9781787128422"],"publisher":"Packt Publishing"}

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

РГБ

Russian State Library [rgb] MARC:
=001 010417613
=005 20201001120855.0
=008 200713s2018\\\\ru\\\\\\\\\\\\000\0\rus\d
=017 \\ $a 7014-20 $b RuMoRGB
=020 \\ $a 978-5-97060-573-8 $c 200 экз.
=040 \\ $a RuMoRGB $b rus $e rcr
=041 1\ $a rus $h eng
=044 \\ $a ru
=084 \\ $a З813.5-02,07 $2 rubbk
=084 \\ $a З973.236-018,07 $2 rubbk
=084 \\ $a З818.6,07 $2 rubbk
=100 1\ $a Джулли, Антонио
=245 00 $a Библиотека Keras - инструмент глубокого обучения : $b реализация нейронных сетей с помощью библиотек Theano и TensorFlow $c Антонио Джулли, Суджит Пал ; пер. с англ. Слинкин А. А.
=260 \\ $a Москва $b ДМК Пресс $c 2018
=300 \\ $a 293 с. $b ил. $c 22 см
=336 \\ $a Текст (визуальный)
=337 \\ $a непосредственный
=500 \\ $a Предм. указ.: с. 290-293
=534 \\ $p Ориг.: $a Gulli, Antonio $t Deep learning with Keras $z 978-1-78712-842-2
=650 \7 $a Техника. Технические науки -- Энергетика. Радиоэлектроника -- Радиоэлектроника -- Кибернетика -- Искусственный интеллект -- Системы искусственного интеллекта -- Проектирование -- Пособие для специалистов $2 rubbk
=650 \7 $a Техника. Технические науки -- Энергетика. Радиоэлектроника -- Радиоэлектроника -- Вычислительная техника -- Вычислительные машины электронные цифровые -- Машины для обучения -- Программирование -- Пособие для специалистов $2 rubbk
=650 \7 $a Техника. Технические науки -- Энергетика. Радиоэлектроника -- Радиоэлектроника -- Кибернетика -- Бионика -- Биоэлектрические модели. Нейронные сети -- Пособие для специалистов $2 rubbk
=700 1\ $a Пал, Суджит
=852 \\ $a РГБ $b FB $j 2 20-43/278 $x 90

Keras is a high-level neural network library written in Python, and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPU and GPU, and allows you to put your ideas into action in the shortest possible time. This book will help you get started with the basics of Keras, in a highly practical manner.
COM004000 - COMPUTERS / Intelligence (AI) & Semantics,COM018000 - COMPUTERS / Data Processing,COM044000 - COMPUTERS / Neural Networks

2020-07-26