Book Image

Deep Learning with Keras [Video]

By : Antonio Gulli, Sujit Pal
Book Image

Deep Learning with Keras [Video]

By: Antonio Gulli, Sujit Pal

Overview of this book

<p><span id="description" class="sugar_field">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 CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner.</span></p> <h2><span class="sugar_field">Style and Approach</span></h2> <p><span class="sugar_field"><span id="trade_selling_points_c" class="sugar_field"><span id="tagline_c" class="sugar_field"><span id="trade_selling_points_c" class="sugar_field">This course is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This course showcases working Deep Neural Networks coded in Python using Keras.</span></span></span></span></p>
Table of Contents (3 chapters)
Chapter 3
Deep Learning with Convolutional Networks
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Section 1
Deep Convolutional Neural Network - DCNN
A deep convolutional neural network (DCNN) consists of many neural network layers. Two different types of layers, convolutional and pooling, are typically alternated. The depth of each filter increases from left to right in the network. The last stage is typically made of one or more fully connected layers. - See the shared weights and bias - Understand pooling layers - Look at the LeNet code in Keras