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)

Deep learning methods

Deep learning is a field of machine learning based on multi-level machine learning. Each level takes the output data of the previous level as input, extracting more and more information as the depth increases. This sequence of learning levels is inspired by the way the mammalian brain processes information and learns by responding to external stimuli. Each level of learning corresponds to one of the different areas that make up the cerebral cortex.

Generic machine learning algorithms behave well on a large number of tasks, managing to solve many important problems. However, they are often not successful in solving central problems concerning the field of artificial intelligence development. The development of deep learning is motivated by the failure of traditional algorithms to perform these tasks.

One area in which deep learning has become more widespread...