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)


In this chapter, we explored the basic concepts of forecasting techniques. Forecast horizon and several forecast methods were analyzed, all in relation to time series data. A time series constitutes a sequence of observations on a phenomenon. In a time series, we can identify several components—trend, seasonality, cycle, and residual. We learned how to remove seasonality from a time series with a practical example.

Then, the models most widely used to represent time series were addressed—AR, MA, ARMA, and ARIMA. For each model, the basic concepts were analyzed and then a mathematical formulation of the model was provided.

Finally, a Keras LSTM model for time series analysis was proposed. Using a practical example, we saw how we can deal with a time series regression problem with a recurrent neural network model of the LSTM type.

In the next chapter, we will...