Book Image

Keras 2.x Projects

By : Giuseppe Ciaburro
Book Image

Keras 2.x Projects

By: Giuseppe Ciaburro

Overview of this book

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
Table of Contents (13 chapters)

Common CNN architecture

These networks have been widely used for various purposes; in the following sections, we will look at some common examples of the use of CNN networks in real-life cases.


LeNet-5 is a convolutional network that was designed by Le Cun in the 1998 for handwritten and machine-printed character recognition. It was the first successful application of convolutional networks. This CNN classifies handwritten numbers, which is why it has been widely applied by banks around the world to recognize handwritten numbers on digitized bank checks in 32 x 32 pixel grayscale images.

The following diagram shows the LeNet-5 architecture, as published by the authors (LeCun, Y., Bottou, L., Bengio, Y., and Haffner...