Advanced Deep Learning with Keras

Conclusion
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.
Chapter 1
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.
References
1. LeCun, Yann, Corinna Cortes, and C. J. Burges. MNIST handwritten digit
database. AT&T Labs [Online]. Available: http://yann. lecun. com/exdb/mnist
2 (2010).
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