### DL - Part - Basic Models

**Perceptron:**

- The perceptron is a fundamental building block of artificial neural networks and is the simplest form of a single-layer neural network.
- It is a binary linear classifier that takes multiple inputs, applies weights to them, and computes a weighted sum.
- The weighted sum is then passed through an activation function (usually a step function) to produce the output, which is either 0 or 1.
- The perceptron learning algorithm adjusts the weights based on the prediction error to learn the optimal decision boundary that separates the input data into different classes.
- Perceptrons are limited to linearly separable problems and cannot handle more complex patterns or non-linear relationships.

**Multi-Layer Perceptron (MLP):**

- The Multi-Layer Perceptron (MLP) is an extension of the perceptron that introduces one or more hidden layers between the input and output layers.
- MLPs can handle more complex patterns and non-linear relationships in data due to the introduction of non-linear activation functions (such as sigmoid, ReLU, or tanh) in the hidden layers.
- Each neuron in the hidden layers computes a weighted sum of inputs, passes it through an activation function, and propagates the output to the next layer.
- MLPs use the backpropagation algorithm to train the network by iteratively adjusting the weights using gradient descent and updating them based on the error gradients.
- MLPs are universal function approximators, meaning they can approximate any continuous function given enough hidden neurons and proper training.