Deep Learning Implementations and Frameworks

Abstract

This tutorial is for AI researchers and practitioners who want to utilize deep learning to develop AI systems or systems making use of AI technologies. It is tailored to help them to choose software frameworks suitable for their applications from various candidates including Caffe and TensorFlow. Choosing an appropriate framework can improve productivity and increase the utility and popularity of work. In this tutorial, the audience will learn guidelines for selecting an appropriate one with the understandings of its features.

This tutorial uniquely discusses design principles that drive the development of deep learning frameworks and shows the pros and cons of adopting each principle or not. Deciding which principle to adopt poses a tradeoff and makes differences for resulting deep learning systems in for example, processing speed, debugging easiness, and the efficiency in dynamic model changes. We examine coding examples for TensorFlow, Keras, and Chainer to give deep understanding of their internal mechanisms, as well as their usage.