In this chapter, we will look into the fundamentals of Python Deep Learning.
Let us now learn about the different deep learning models/ algorithms.
Some of the popular models within deep learning are as follows −
The inputs and outputs are represented as vectors or tensors. For example, a neural network may have the inputs where individual pixel RGB values in an image are represented as vectors.
The layers of neurons that lie between the input layer and the output layer are called hidden layers. This is where most of the work happens when the neural net tries to solve problems. Taking a closer look at the hidden layers can reveal a lot about the features the network has learned to extract from the data.
Different architectures of neural networks are formed by choosing which neurons to connect to the other neurons in the next layer.
Following is the pseudocode for calculating output of Forward-propagating Neural Network −
For each neuron x, from left to right −