Deep Learning Basics and Environment Setup

In this chapter, we offer you essential knowledge for building and training deep learning models, including Generative Adversarial Networks (GANs). We are going to explain the basics of deep learning, starting with a simple example of a learning algorithm based on linear regression. We will also provide instructions on how to set up a deep learning programming environment using Python and Keras. We will also talk about the importance of computing power in deep learning; we are going to describe guidelines to fully take advantage of NVIDIA GPUs by maximizing the memory footprint, enabling the CUDA Deep Neural Network library (cuDNN), and eventually using distributed training setups with multiple GPUs. Finally, in addition to installing the libraries that will be necessary for upcoming projects in this book, you will test your installation by building, from scratch, a simple and efficient Artificial Neural Network (ANN) that will learn from data how to classify images of handwritten digits.

The following major topics will be covered in this chapter:

• Deep learning basics
• Deep learning environment setup
• The deep learning environment test