Transformers: Self-attention
In the realm of neural networks, three primary types are commonly discussed: Artificial Neural Networks (ANNs) : These are fully connected networks comprising input, hidden, and output layers. Each neuron in a layer is connected to every neuron in the subsequent layer, enabling complex pattern recognition through weighted connections. Convolutional Neural Networks (CNNs) : CNNs incorporate convolutional layers with kernels (filters) that slide across the input data to detect features. These networks also perform pooling operations to reduce dimensionality and flatten the data before passing it to fully connected layers. CNNs are particularly effective for image and spatial data processing. Recurrent Neural Networks (RNNs) : RNNs are designed to handle sequential data by maintaining a memory of previous inputs through their hidden states. Unlike feed-forward networks, RNNs can process input sequences of variable length, making them suitable for tasks involving time series data, nat...