Understanding Feed Forward Neural Networks With Maths and Statistics

Gradient-based backpropagation algorithms get used to training feed forward neural networks (FNN). The backpropagation algorithm makes up the basis of most neural networks. A neural network can get trained effectively using the chain rule algorithm. The backpropagation algorithm adjusts the parameters of the network after each pass through the network (weights and biases).

In a feed forward neural network, nodes never form cycles in the network. The input layer of this neural network receives inputs, and the output layer produces outputs.

Following are some of the applications of feed forward neural networks.:

• Data compression.
• Pattern recognition.
• Computer vision.
• Sonar target recognition.
• Speech recognition.
• Handwritten character recognition.

The nine types of neural networks are:

• Perceptron
• Feed forward neural network
• Multilayer perceptron
• Convolutional neural network
• Radial basis function neural network
• Recurrent neural network
• LSTM – long short-term memory
• Sequence to sequence models
• Modular neural network

Among the non-linear activation functions used in deep learning, the ReLU function is one of the most popular. It is an abbreviation for Rectified Linear Unit.
ReLU functions are more efficient than others in activating neurons simultaneously because they don't activate all neurons at once.

Backpropagation is an algorithm for training neural networks (adjusting their weights). Backpropagation uses output_vector as input, target_output_vector as target, and adjusted_weight_vector as output.

Feed forward is an algorithm to calculate the output vector from the input vector. Input for feed forward is input_vector, whereas the output is output_vector.