Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn complex patterns from large amounts of data. Unlike traditional ML, which often requires manual feature extraction, deep learning automatically learns features during training. It excels in tasks like image recognition, natural language processing, and speech recognition. The key difference lies in deep learning’s ability to handle unstructured data and scale with massive datasets.

A neural network is made up of layers of interconnected nodes: an input layer, one or more hidden layers, and an output layer. Each node (neuron) processes inputs, applies weights, biases, and an activation function to produce an output. During training, the network learns by adjusting weights using algorithms like backpropagation and gradient descent, minimizing the error between predicted and actual outputs. This layered learning process enables the network to detect complex patterns in data.

Deep learning is widely used in real-world domains. In computer vision, it powers facial recognition, object detection, and medical image analysis. In natural language processing (NLP), it enables chatbots, language translation, and sentiment analysis. In automation, it drives autonomous vehicles, robotics, and smart assistants. These applications showcase how deep learning transforms raw data into intelligent actions across industries.