Chapter 2 - Getting to Know TensorFlow

1. Every computation in TensorFlow is represented by a computational graph. It consists of several nodes and edges, where nodes are the mathematical operations, such as addition, multiplication, and so on, and edges are the tensors. A computational graph is very efficient in optimizing resources and it also promotes distributed computing.
2. A computational graph with the operations on the node and tensors to its edges will only be created, and in order to execute the graph, we use a TensorFlow session.
3. A TensorFlow session can be created using tf.Session(), and it will allocate the memory for storing the current value of the variable.
4. Variables are the containers used to store values. Variables will be used as input to several other operations in the computational graph. We can think of placeholders as variables, where we only define the type...