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)

What is Next?

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In this chapter, we will summarize what has been covered in this book so far, and what the next steps are from this point onward. You will look at how to apply the skills you have gained to other projects, real-life challenges in building and deploying Keras deep learning models, and other common technologies that data scientists often use. By the end of this chapter, you will have a better understanding of the real-life challenges in building and deploying deep learning models and the additional resources and technologies you will need to sharpen your deep learning skills. In addition, you'll find out what some of the challenges are that await deep learning researchers in the near future.

We will cover the following topics in this chapter:

  • Deep learning methods
  • Automated machine learning
  • Differentiable neural computers...