Summary

In this chapter, you learned about the basics of deep learning, including the common representations and terminology and essential underlying concepts. You learned how forward propagation in neural networks works and how it is used for predicting outputs. You learned about the loss function as a measure of model performance and learned how backpropagation is used to compute the derivatives of loss function with respect to model parameters. Finally, you learned about gradient descent, which uses the gradients computed by backpropagation to gradually update the model parameters. In addition to basic theory and concepts, you also learned how to implement and train shallow and deep neural networks with Keras and how to use a trained network to make predictions about the output of a given input. You also learned how to evaluate the overall performance of the network over all data examples, and the reasons why evaluating a model on training examples can be misleading. You learned about...