Deep Learning

Abstract

Deep learning is a field of machine learning that is a true enabler of cutting-edge achievements in the domain of artificial intelligence. The term deep implies a complex structure that is designed to handle massive datasets using intensive parallel computations (mostly by leveraging clusters of GPU-equipped machines). The term learning in this context means that feature engineering and customization of model parameters are left to the machine. In practice, the combination of these terms in the form of deep learning implies multilayered neural networks. Neural networks are heavily used for tasks like image classification, voice recognition/synthetization, time series analysis, and so forth. Neural networks tend to mimic how our brain cells work in tandem in decision-making activities. This chapter introduces you to neural networks and how to build them using PyTorch, which is an open-source Python framework (visit https://pytorch.org ) that has a familiar API to those accustomed to Numpy. Furthermore, as the last chapter in this book, it exemplifies many stages of the data science life cycle model (data preparation, feature engineering, data visualization, data analysis, and data product deployment). First, though, let’s consider the notion of intelligence as well as when, how, and why it matters.