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)

Automated machine learning

Automated machine learning (AutoML) refers to those applications that are able to automate the end-to-end process of applying machine learning to real-world problems. Generally, scientific analysts must process data through a series of preliminary procedures before submitting them to machine learning algorithms. In previous chapters, we have seen the necessary steps for performing proper analysis of data through these algorithms. We have seen how simple it is to build a model based on deep neural networks using Keras. In some cases, these skills are outside those possessed by analysts, who must seek support from industry experts to solve the problem. AutoML was born from the need to create an application that automates the whole machine learning process, so that the user can take advantage of these services.

Generally, machine learning experts must perform...