Considering TensorFlow for the Enterprise

Chapter 1. Choosing to Use Deep Learning

The first questions an enterprise must ask before it adopts this new technology are what is deep learning and why make the change? For the first question, Microsoft Research’s Li Deng succinctly answers:¹

[d]eep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of nonlinear information processing in hierarchical architectures are exploited for pattern classification and for feature learning.

The terminology “deep” refers to the number of hidden layers in the network, often larger than some relatively arbitrary number like five or seven.

We will not dwell on this question, because there are many books and articles available on deep learning. However, the second question remains: if existing data science pipelines are already effective and operational, why go through the effort and consume the organizational resources to make this transition?

General Rationale

From a general perspective, there is a strong argument to be made for investing in deep learning. True technological revolutions—those that affect multiple segments of society—do so by fundamentally changing the cost curve of a particular capability or task. Let’s consider the conventional microprocessor as an example. Before computers, performing mathematical calculations (think addition, multiplication, square roots, etc.) was expensive and time consuming for people to do. With the advent of the digital computer, the cost ...