This chapter provided an overview of the three deep learning models – MLPs, RNNs, CNNs – and also introduced Keras, a library for the rapid development, training and testing those deep learning models. The sequential API of Keras was also discussed. In the next chapter, the Functional API will be presented, which will enable us to build more complex models specifically for advanced deep neural networks.
This chapter also reviewed the important concepts of deep learning such as optimization, regularization, and loss function. For ease of understanding, these concepts were presented in the context of the MNIST digit classification. Different solutions to the MNIST digit classification using artificial neural networks, specifically MLPs, CNNs, and RNNs, which are important building blocks of deep neural networks, were also discussed together with their performance measures.
With the understanding of deep learning concepts, and how Keras can be used as a tool with them, we are now equipped to analyze advanced deep learning models. After discussing Functional API in the next chapter, we'll move onto the implementation of popular deep learning models. Subsequent chapters will discuss advanced topics such as autoencoders, GANs, VAEs, and reinforcement learning. The accompanying Keras code implementations will play an important role in understanding these topics.