The Deep Learning Framework
Shriram K. Vasudevan, Sini Raj Pulari, Subashri Vasudevan · 2021
This chapter covers the basics of the Deep Learning framework. It starts with explaining about a neuron and a biological neuron followed by the structure of a simple neural network. It then takes the readers through the Perceptron, which is the unit in the artificial neural network that acts as the computational unit for extracting features. Then it touches upon the different activation functions such as ReLu, Softmax and tanH. Parameters are the most important concept in any Deep Learning model. This chapter covers the basics of parameters and of different types of parameters. Finally, it discusses the Optimizer functions, which are important for any model. The chapter ends with key points and a quiz to test the readers’ understanding.