Convolutional and recurrent neural networks
Convolutional Neural Networks (CNNs) are specialized for processing grid-like data such as images. They use convolutional layers that apply filters to detect patterns like edges, textures, and objects. Pooling layers reduce spatial dimensions while preserving important features. CNNs have revolutionized computer vision tasks like image classification, object detection, and segmentation. Recurrent Neural Networks (RNNs) are designed for sequence data such as time series, text, and speech. They maintain a hidden state that captures information about previous elements in the sequence. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are RNN variants that address the vanishing gradient problem and can learn long-range dependencies. Understanding CNNs and RNNs enables solving complex problems in computer vision, natural language processing, and time series analysis.