I asked my GPT Model what are the different types of Neural Networks and this is what it wrote for me:

**What is a Convolutional Neural Network (CNN)?** A CNN is a type of ANN that models image recognition, like a computer recognizes a picture. The network includes a set of layers, each with a set of convolutional and pooling layers.

More precisely, the first two layers are convolutional layers, followed by an output layer and then a pooling layer. Each convolutional layer reduces the size of the input image by a factor of two, with the last layer being set to a threshold, in which case the image is recognized as black or white and treated accordingly.

**What is Perceptron?** A Perceptron is an ANN that is similar to a human brain, but the connections between neurons are more complicated. In a perceptron, every neuron has a weight and an associated bias.

When a signal is applied to a neuron, the neuron receives an input weight and a bias, and responds to this signal by changing its weight and its bias. The goal of the network is to recognize a signal by comparing the weight and bias of each neuron to the target signal.

**What is Belief Net?** Belief nets are artificial neural networks used in the field of machine learning, that mimic the way the brain learns by training itself. For example, the machine is trained to recognize the shape of a circle. Each neuron in the network has a weight, which is a real number the neuron compares with the target shape.

**What is Recurrent Neural Network (RNN)?** A Recurrent Neural Network (RNN) is a type of ANN that repeats its computation back to the beginning. For example, the goal is for an RNN to determine if the input is a circle or a line. First, each neuron in the network is given a weight and an associated bias. This is the input, and the network is trained to recognize the pattern of weights and biases.

**What is LSTM?** A LSTM is an ANN that uses long short-term memories to process the training data. Each time the network is fed the data, the weights and biases are reset. For example, suppose that you want to predict whether the car is red or blue. First, each neuron in the network is given a weight and an associated bias. This is the input, and the network is trained to recognize the pattern of weights and biases.