Deep Learning

Deep learning is a subfield of machine learning concerned with training artificial neural networks with multiple layers, also known as deep neural networks, to learn and extract patterns from large and complex datasets. It has revolutionized numerous disciplines, including computer vision, natural language processing, and speech recognition. Fundamentally, deep learning employs statistical models to capture and represent the underlying data relationships. In deep learning, the statistical model typically consists of a network of interconnected nodes, where each node performs a mathematical operation on the input data and transfers it to an activation function to generate an output. These models employ statistical techniques such as gradient descent optimization, backpropagation, and stochastic gradient descent to iteratively modify the model's parameters and minimize the gap between predicted and actual values. Using these statistical models, deep learning algorithms can learn complex patterns, generalize from examples, and make accurate predictions or classifications. Deep learning's capacity to autonomously learn and extract features from unstructured data without explicit feature engineering has led to its widespread adoption in artificial intelligence.