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
Content Locked
Section 2
Recognizing CIFAR-10 Images with Deep Learning
The CIFAR-10 dataset contains 60,000 color images of 32 x 32 pixels in 3 channels divided into 10 classes. Each class contains 6,000 images. The training set contains 50,000 images, while the test sets provides 10,000 images. - Recognize previously unseen images and assign them to one of the 10 classes - Improve the CIFAR-10 performance with deeper a network - Improve the CIFAR-10 performance with data augmentation