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)

Convolutional neural networks

As detailed in Chapter 4, Concrete Quality Prediction Using Deep Neural Networks, artificial neural networks (ANNs) are inspired from biological neural networks: starting from the mechanisms regulating natural neural networks, they intended to simulate human thinking. They are used to estimate or approximate functions that may depend on a large number of inputs, many of which are often unknown. ANNs are generally introduced as interconnected neuron systems, among which an exchange of messages takes place. Each connection has a related weight; the value of the weight is adjustable based on experience, and this makes neural networks an instrument that's adaptable to the various types of input and having the ability to learn, as depicted in the following diagram:

ANNs define the neuron as a central processing unit (CPU), which performs a mathematical...