In Chapter 13, Parallelizing Neural Network Training with TensorFlow, we trained a multilayer perceptron to classify MNIST digits, using various aspects of the TensorFlow Python API. That was a great way to dive us straight into some hands-on experience with TensorFlow neural network training and machine learning.
In this chapter, we'll now shift our focus squarely on to TensorFlow itself, and explore in detail the impressive mechanics and features that TensorFlow offers:
Key features and advantages of TensorFlow
TensorFlow ranks and tensors
Understanding and working with TensorFlow graphs
Working with TensorFlow variables
TensorFlow operations with different scopes
Common tensor transformations: working with ranks, shapes, and types
Transforming tensors as multidimensional arrays
Saving and restoring a model in TensorFlow
Visualizing neural network graphs with TensorBoard
We'll stay hands-on in this chapter, of course, and implement graphs throughout...