Can be complicated sometimes, eh? Let’s make it super simple for you!

Structure of neural networks

– Input neurons represents the information we are trying to classify. – Each number in the input neurons is given a weight at each synapse – At each neuron in the next layer, we add the outputs of all synapses coming to that neuron along with a bias and apply an activation function to the weighted sum (this number is somewhere between 0 and 1) – Each yellow node in the hidden layer is a weighted sum of the blue input node values. The output is a weighted sum of the yellow nodes. – The output of that function will be treated as the input for the next synapse layer. – Continue until you reach the output.

Some good YouTube videos to understand this: (1) https://lnkd.in/dCeAQBm (2) https://lnkd.in/dckaczW The videos also contain good examples that will help you understand the concept better.

Feed forward neural networks!

– These were the first type of neural networks invented, are usually simpler than other networks. – They just go in one direction and the connection between the units do not form a cycle. – Use cases : Mostly in Supervised learning (where the data to be learned is neither sequential nor time-dependent).

Single-Layer Perceptrons – Simplest type of feedforward neural network. – They have no hidden units. – The output units are computed directly from the sum of the product of their weights with the corresponding input units, plus some bias. – The single-layer perceptrons are linear classifiers, hence they can only learn linearly separable patterns.

Multi-Layer Perceptron (MLP) – MLP is composed of many perceptrons. – Layers in MLPs comprise of an input layer, some number (or zero) of hidden layers, and an output layer. – Unlike single-layer perceptrons, MLPs are capable of learning to compute non-linearly separable functions. – Use cases: One of the primary machine learning techniques for both regression and classification in supervised learning.