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)

Creating a linear regression model

A simple linear regression is easy to understand, but represents the basis of regression techniques. Once these concepts are understood, it will be easier for us to address the other types of regression. To begin with, let's take an example of applying linear regression that's been taken from the real world.

Consider some data that has been collected on a group of bikers, which consists of the following aspects:

  • Number of years of use
  • Number of kilometers traveled in one year
  • Number of falls

Through these techniques, we find that, on average, when the number of kilometers traveled increases, the number of falls also increases. By increasing the number of years of motorcycle usage and by increasing the experience, the number of falls tends to decrease.

The linear regression method consists of precisely identifying a line that is capable...