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)

Optical character recognition

We have always been particularly sensitive to the problem of the automatic recognition of writing in order to achieve a simpler interaction between humans and machines. Especially in the last few years, this problem has found interesting developments and more and more efficient solutions thanks to a very strong economic interest and an ever-greater capacity to process the data of modern computers. In particular, some countries, such as Japan, and Asian countries in general, are investing heavily in terms of research and financial resources, making state-of-the-art OCR.

It is easy to understand the interest of these countries in this field of research. In fact, we try to create devices able to interpret the ideograms characteristic of those cultures to allow greater comfort in the interaction with the machines. Since there are currently no input devices...