The recent advancements in deep learning can be, to some extent, attributed to the advancements in computing power. The increase in computing power, more specifically the use of GPUs for processing data, has contributed to the leap from shallow neural networks to deeper neural networks. In this chapter, we lay the groundwork for all following chapters by showing you how to set up stable environments for different deep learning frameworks used in this cookbook. There are many open source deep learning frameworks that are used by researchers and in the industry. Each framework has its own benefits and most of them are supported by some big tech company.
By following the steps in this first chapter carefully, you should be able to use local or cloud-based CPUs and GPUs to leverage the recipes in this book. For this book, we've used Jupyter Notebooks to execute all code blocks. These notebooks provide interactive feedback per code block in such a way that it's perfectly suited for storytelling.
The download links in this recipe are intended for an Ubuntu machine or server with a supported NVIDIA GPU. Please change the links and filenames accordingly if needed. You are free to use any other environment, package managers (for example, Docker containers), or versions if needed. However, additional steps may be required.
