Artificial Neural Network (ANN):

An ANN is a computational model inspired by the brain, comprising interconnected nodes. Common types include:

1. Feedforward Neural Network (FNN): Simple, one-way flow for classification.

2. Multilayer Perceptron (MLP): Has hidden layers; used for various applications.

3. Convolutional Neural Network (CNN): Ideal for image processing, using convolutional layers.

4. Recurrent Neural Network (RNN): Processes sequential data with feedback loops.

5. Long Short-Term Memory (LSTM): A type of RNN, effective for long-term dependencies.

6. Gated Recurrent Unit (GRU): Simplified version of LSTM, efficient for sequential data.

7. Autoencoder: Unsupervised learning for data compression and reconstruction.

8. Generative Adversarial Network (GAN): Involves a generator and discriminator for creating synthetic data.

9. Radial Basis Function Network (RBFN): Uses radial basis functions; common in pattern recognition.

10. Hopfield Network: A recurrent network for associative memory tasks.

These networks cater to diverse tasks, with architecture chosen based on the specific problem.

Dear Doctor

"Neural Networks are networks used in Machine Learning that work similar to the human nervous system. It is designed to function like the human brain where many things are connected in various ways.  Artificial Neural Networks find extensive applications in areas where traditional computers don’t fare too well. There are many kinds of artificial neural networks used for the computational model.

Top 7 Artificial Neural Networks in Machine Learning

1. Modular Neural Networks

2. Feedforward Neural Network – Artificial Neuron

3. Radial basis function Neural Network

4. Kohonen Self Organizing Neural Network

5. Recurrent Neural Network(RNN)

6. Convolutional Neural Network

7. Long / Short Term Memory"

An Artificial Neural Network (ANN) is a computational model inspired by the way biological neural networks in the human brain function. It consists of interconnected nodes, often referred to as neurons or artificial neurons, organized in layers. These layers include an input layer, one or more hidden layers, and an output layer. Each connection between neurons has an associated weight, and during the learning process, these weights are adjusted based on the network's performance on training data.

Here are some commonly used types of Artificial Neural Networks:

These are just a few examples, and there are many other specialized types of neural networks designed for specific tasks and applications. The choice of the neural network architecture depends on the nature of the data and the problem at hand.

Overview ANN:

Structure: ANNs are composed of nodes or neurons, which are connected by edges. Each neuron receives input, processes it, and passes its output to the next layer of neurons.

Layers: There are typically three types of layers in an ANN:

Input Layer: Receives the initial data for processing.

Hidden Layers: Intermediate layers that process inputs received from the previous layers and pass the output to the next layer. The complexity of the ANN depends on the number of hidden layers and neurons within them.

Output Layer: Produces the final output of the network.

Learning Process: ANNs learn by adjusting the weights of connections based on the input they receive and the output they are supposed to generate.

Examples:

- Feedforward Neural Networks (FNN)

- Convolutional Neural Networks (CNN)

- Recurrent Neural Networks (RNN)

- Long Short-Term Memory (LSTM)

- Generative Adversarial Networks (GANs)