Deep learning (DL)

Deep learning models, particularly deep neural networks (DNNs), are composed of interconnected nodes (neurons) organized in layers. The first layer (input layer) receives the raw data, subsequent layers (hidden layers) perform feature extraction and transformation, and the final layer (output layer) produces the result. Each connection between neurons has a weight associated with it, which is adjusted during the training process using algorithms like backpropagation. Backpropagation calculates the gradient of the loss function with respect to the weights and biases of the network, and then updates the weights to minimize the loss. The 'deep' in deep learning refers to the multiple hidden layers, which enable the network to learn increasingly abstract and complex features from the data. Different architectures, such as Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data, are designed to excel in specific tasks. Deep learning's ability to automatically learn features from raw data, without explicit feature engineering, has led to significant breakthroughs in various AI applications. It requires large amounts of labeled data and significant computational resources for training.