Going Deeper – The Mechanics of TensorFlow

In Chapter 13, Parallelizing Neural Network Training with TensorFlow, we covered how to define and manipulate tensors and worked with the tf.data API to build input pipelines. We further built and trained a multilayer perceptron to classify the Iris dataset using the TensorFlow Keras API (tf.keras).

Now that we have some hands-on experience with TensorFlow neural network (NN) training and machine learning, it's time to take a deeper dive into the TensorFlow library and explore its rich set of features, which will allow us to implement more advanced deep learning models in upcoming chapters.

In this chapter, we will use different aspects of TensorFlow's API to implement NNs. In particular, we will again use the Keras API, which provides multiple layers of abstraction to make the implementation of standard architectures very convenient. TensorFlow also allows us to implement custom NN layers, which is very useful in...