Summary

We covered a lot of different things in this chapter. We started by learning the basics of a neural network and then we gradually proceeded. We learned the two most powerful types of neural networks used today—CNNs and RNNs—and we also learned about them on a high level, but without skipping their foundational units. We learned that as the complexity in a neural network increases, it requires a lot of computational power, which standard computers may fail to cater for we saw how this problem can be overcome by configuring a deep learning development environment using two different providers—AWS and Crestle. We explored Jupyter Notebooks, a powerful tool for performing deep learning tasks. We learned about the usage of two very popular Python libraries—NumPy and pandas. Both of these libraries are extensively used when performing deep learning tasks.

In the next chapter, we will be building applications and integrating deep learning to make them perform intelligently. But before we did this, it was important for us to know the basics that were covered in this chapter. We are now in a good position to move on to the next chapter.