Summary

In this chapter, we introduced the most fundamental type of deep learning model—the MLP. We covered a lot of new concepts related to this power class of models such as deep learning, neural network models, and the activation functions of neurons. We also learned about TensorFlow, which is a framework to train deep learning models; we used it as a backend for running the calculations necessary to train our models. We covered Keras, where we first build a network, and then we compile it (indicating the loss and optimizer), and finally, we train the model. Lastly, we covered dropout, which is a regularization technique that is often used with neural networks, although it works best with very large networks. To conclude, neural networks are hard to train because they involve making many decisions; a lot of practice and knowledge is needed to be able to use these models...