Artificial Neural Networks (ANNs) are computational models inspired by the human brain, consisting of interconnected nodes (neurons) that process information.
Thank you for reading this post, don't forget to subscribe!Different types of Artificial Neural Networks are used depending on the complexity and nature of the task:
- Feed-Forward Neural Network (FNN)
- Recurrent Neural Network (RNN)
- Single-Layer Neural Network
- Multi-Layer Neural Network
1.) Feed-Forward Neural Network (FNN):
A Feed-Forward Neural Network (FNN) is the simplest type of ANN where information flows in one direction—from input to output, without any feedback loops.
How it Works:
- The network consists of an input layer, hidden layers (optional), and an output layer.
- Each neuron applies an activation function (e.g., ReLU, Sigmoid) to transform inputs into outputs.
- No cycles or loops exist in the network structure.
Advantages:
- Simple and easy to implement.
- Works well for classification and regression tasks.
Applications:
- Handwritten digit recognition (e.g., MNIST dataset).
- Spam email filtering.
2.) Recurrent Neural Network (RNN):
A Recurrent Neural Network (RNN) is designed for sequential data processing.
- Unlike Feed-Forward Networks, RNNs have feedback loops, meaning they can remember past inputs and use them to influence future outputs.
How it Works:
- The network includes connections that loop back, allowing information to be stored over time.
- It processes one data point at a time and maintains a hidden state that captures previous inputs.
- Uses activation functions like Tanh and ReLU for learning patterns over time.
Advantages:
- Suitable for time-series and sequential data.
- Can model dependencies in language and speech processing.
Applications:
- Speech recognition (e.g., Google Voice Assistant).
- Machine translation (e.g., Google Translate).
- Stock price prediction.
3.) Single-Layer Neural Network:
A Single-Layer Neural Network consists of only one layer of neurons between the input and output.
How it Works:
- Each neuron in the input layer is directly connected to the output neurons.
- Uses linear and simple activation functions to process data.
- It is limited to solving linearly separable problems (e.g., AND, OR logic gates).
Advantages:
- Computationally efficient.
- Works well for simple classification tasks.
Limitations:
- Cannot handle complex problems like XOR classification.
- Not suitable for deep learning tasks.
Applications:
- Spam filtering (basic email classification).
- Optical character recognition (OCR).
4.) Multi-Layer Neural Network (MLNN):
A Multi-Layer Neural Network (MLNN) has multiple hidden layers between the input and output, allowing it to learn complex patterns.
How it Works:
- It consists of multiple layers of neurons, each applying activation functions.
- Uses Backpropagation to adjust weights and minimize errors.
- Can extract features and perform deep learning tasks.
Advantages:
- Solves non-linearly separable problems (e.g., XOR classification).
- Enables deep learning for complex applications.
Applications:
- Image recognition (e.g., facial recognition in smartphones).
- Self-driving cars (e.g., Tesla Autopilot).
- Fraud detection in banking.