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)

Different types of classification

The power of the classification methods is due to the quality of its algorithms, which have been improved and updated over the years. These are divided into several main types, depending on the nature of the signal used for learning or the type of feedback adopted by the system.

They are as follows:

  • Supervised learning: The algorithm generates a function that links input values to a desired output through the observation of a set of examples, in which each piece of data that's input has its relative output data. This is used to construct predictive models.
  • Unsupervised learning: The algorithm tries to derive knowledge from a general input, without the help of a set of preclassified examples, which are used to build descriptive models. Typical examples of the application of these algorithms are search engines.

The following diagram shows...