In the previous chapter, we understood how a neural network works, what the various hyper parameters in a neural network are, and how they can be tweaked further to improve our model's accuracy.
Google offers TensorBoard, a visualization of the model training logs. In this chapter, we show how to use TensorBoard for TensorFlow and Keras. We interpret the visualizations generated by TensorBoard to understand the performance of our models, and also understand the other functionalities in TensorBoard that can help visualize our dataset better.
As discussed in the previous chapter, Keras as a framework is a wrapper on top of either TensorFlow or Theano. The computations that you'll use TensorFlow for, such as training a massive deep neural network, can be complex and confusing. To make it easier to understand, debug, and optimize TensorFlow...
