,In this chapter, we will explain the main computational concept behind TensorFlow, which is the computational graph model, and demonstrate how to get you on track by implementing linear regression and logistic regression.

The following topics will be covered in this chapter:

• Capacity of a single neuron and activation functions
• Activation functions
• Feed-forward neural network
• The need for a multilayer network
• TensorFlow terminologies—recap
• Linear regression model—building and training
• Logistic regression model—building and training

We will start by explaining what a single neuron can actually do/model, and based on this, the need for a multilayer network will arise. Next up, we will do more elaboration of the main concepts and tools that are used/available within TensorFlow and how to use these tools to build up simple examples such as linear regression and logistic regression.