Deep learning has revolutionized the field of data science and it is still making progress, but there are still major industries that are yet to experience all of the advantages of deep learning, such as the medical and manufacturing industries. The zenith of human achievement will be to create a machine that can learn as humans do and that can become an expert in the same way humans can. Successful deep learning, though, usually comes with the prerequisite of having very large datasets to work from. Fortunately, this book focuses on architectures that can do away with this prerequisite.
In this chapter, we learned about the human brain and how the structure of an artificial neural network is close to the structure of our brain. We introduced the basic concepts of machine learning and deep learning, along with their challenges. We also discussed one-shot learning and its various types, and later experimented with the iris dataset to compare a parametric and nonparametric approach in a scarce data situation. Overall, we concluded that proper feature representation plays an important role in determining the efficiency of a machine learning model.
In the next chapter, we will learn about metrics-based one-shot learning methods and explore the feature extraction domain of one-shot learning algorithms.
