Deep learning software frameworks

Every good deep learning application needs to have several components to be able to function correctly. These include:

• A model layer which allows a developer to design his or her own model with more flexibility
• A GPU layer that makes it seamless for application developers to choose between GPU/CPU for its application
• A parallelization layer that can allow the developer to scale his or her application to run on multiple devices or instances

As you can imagine, implementing these modules is not easy. Often a developer needs to spend more time on debugging implementation issues rather than the legitimate model issues. Thankfully, a number of software frameworks exist in the industry today which make deep learning application development practically the first class of its programming language.

These frameworks vary in architecture, design, and feature but almost all of them provide immense value to developers by providing them easy and fast implementation framework for their applications. In this section, we will take a look at some popular deep learning software frameworks and how they compare with each other.