2 TensorFlow 2

This chapter covers

What is TensorFlow and main differences between TensorFlow 1 and TensorFlow 2
Various features introduced in TensorFlow 2 such as AutoGraph and Eager Exectution
Various data structures and operations in TensorFlow such as, tf.Variable and tf.Tensor
Common neural network related operations in TensorFlow

We now know that TensorFlow is an end-to-end machine learning framework predominantly used for implementing deep neural networks. TensorFlow is skillful at converting these deep neural networks to computational graphs that run faster on optimized hardware (e.g. Graphical Processing Units - GPUs and Tensor Processing Units - TPUs). But keep in mind that this is not the only use for TensorFlow. Table 2.1 delineates other areas TensorFlow supports.

2.1 TensorFlow 2 vs TensorFlow 1

2.1.1 How does TensorFlow operate under the hood?

2.1.2 Visiting an old friend: TensorFlow 1

2.2 Building blocks in TensorFlow

2.2.1 Understanding tf.Variable

2.2.2 Understanding tf.Tensor

2.2.3 Understanding tf.Operation

2.3 Neural network related computations in TensorFlow

2.3.1 Matrix multiplication

2.3.2 Convolution operation

2.3.3 Pooling operation

2.4 Summary

2.5 Answers to exercises