9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras.
Keras 2.x Projects explains how to leverage the power of Keras to build and train state-ofthe-
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.