Neural Network Architectures

Neural networks are the foundation of deep learning. They consist of layers of interconnected artificial neurons that transform input data into meaningful representations. There are several key architectures used in deep learning:

• Feedforward Neural Networks (FNN): The simplest type of neural network, where information moves in one direction from input to output.
• Convolutional Neural Networks (CNN): Primarily used for image processing, CNNs leverage spatial hierarchies in data through convolutional layers.
• Recurrent Neural Networks (RNN): Designed for sequential data, RNNs can process time-series data and natural language sequences.
• Long Short-Term Memory Networks (LSTMs): A specialized type of RNN that solves the vanishing gradient problem and improves long-term dependency learning.
• Transformers: Advanced architectures, such as BERT and GPT, that use attention mechanisms for natural language processing.

Each architecture is designed for specific use cases, allowing deep learning models to excel in complex tasks such as image classification, language translation, and speech recognition.