In this chapter, we covered in detail the key features and concepts of TensorFlow. We started with discussing TensorFlow's main features and advantages, and key TensorFlow concepts such as ranks and tensors. We then looked at TensorFlow's computation graphs, and discussed how to launch a graph in a session environment, and you learned about placeholders and variables. We then saw different ways to evaluate tensors and execute operators, using Python variables or by referring to them via their name in the graph.

We went further to explore some of the essential TensorFlow operators and functions for transforming tensors, such as tf.transpose, tf.reshape, tf.split, and tf.concat. Finally, we saw how to visualize a TensorFlow computation graph using TensorBoard. Visualizing computation graphs using this module can be very useful, especially when we are debugging complex models.

In the next chapter, we'll make use of this library to implement an advanced image classifier: a Convolutional Neural Network (CNN). CNNs are powerful models and have shown great performance in image classification and computer vision. We'll cover the basic operations in CNNs, and we'll implement deep convolutional networks for image classification using TensorFlow.