Summary

In this chapter, we were introduced to a subset of machine learning—deep learning. You learned about the differences and similarities between the two categories of techniques and understood the requirement for deep learning and its applications.

Neural networks are artificial representations of the biological neural networks that are present in the human brain. Artificial neural networks are frameworks that are incorporated by deep learning models and have proven to be increasingly efficient and accurate. They are used in several fields, from training self-driving cars to detecting cancer cells in very early stages.

We studied the different components of a neural network and learned a network trains and corrects itself, with the help of the loss function, the gradient descent algorithm and backpropagation. You also learned how to perform sentiment analysis on text inputs! Furthermore, you learned the basics of deploying a model as a service.

In the coming chapters, you will learn...