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)

Time series analysis

A time series constitutes a sequence of observations on a phenomenon that's carried out in consecutive instants or time intervals that are usually, even if not necessarily, evenly spaced or of the same length. The trend of commodity prices, stock market indices, the BTP/BUND spread, and the unemployment rate are just a few examples of times series.

Contrary to what happens in classical statistics, where it is assumed that independent observations come from a single random variable, in a time series, it is assumed that there are n observations coming from as many dependent random variables as possible. The inference of the time series is thus configured as a procedure that attempts to bring the time series back to its generating process.

The time series can be of two types:

  • Deterministic: If the values of the variable can be exactly determined on the...