In this chapter, you learned how techniques in the field of AI have evolved into deep learning. We now know that there were two booms in AI and that we are now in the third boom. Searching and traversing algorithms were developed in the first boom, such as DFS and BFS. Then, the study focused on how knowledge could be represented with symbols that a machine could easily understand in the second boom.
Although these booms had faded away, techniques developed during those times built up much useful knowledge of AI fields. The third boom spread out with machine learning algorithms in the beginning with those of pattern recognition and classification based on probabilistic statistical models. With machine learning, we've made great progress in various fields, but this is not enough to realize true AI because we need to tell a machine what the features of objects to be classified are. The technique required for machine learning is called feature engineering. Then, deep learning came out, based on one machine learning algorithm - namely, neural networks. A machine can automatically learn what the features of objects are with deep learning, and thus deep learning is recognized as a very innovative technique. Studies of deep learning are becoming more and more active, and every day new technologies are invented. Some of the latest technologies are introduced in the last chapter of this book, Chapter 8, What's Next?, for reference.
Deep learning is often thought to be very complicated, but the truth is it's not. As mentioned, deep learning is the evolving technique of machine learning, and deep learning itself is very simple yet elegant. We'll look at more details of machine learning algorithms in the next chapter. With a great understanding of machine learning, you will easily acquire the essence of deep learning.
