Summary

We kicked off a new section of the book with an introduction to deep learning. We started with a bit of history to understand why deep learning is so popular today and we also explored its humble beginnings in Perceptron. We understood the composability of deep learning and understood and dissected the different components of deep learning such as the representation learning block, linear layers, activation functions, and so on. Finally, we rounded off the discussion by looking at how a deep learning system uses gradient descent to learn from data. With that understanding, we are now ready to move on to the next chapter, where we will drive the narrative toward time series models.