Deep learning is a subset of machine learning and a part of artificial intelligence, allowing machines to mimic human behavior. It uses artificial neural networks inspired by the human brain's structure to process information. Unlike traditional machine learning, which requires manual feature extraction, deep learning enables neural networks to autonomously identify features from raw data. For instance, when differentiating between tomatoes and cherries, machine learning relies on pre-defined features, while deep learning learns to identify them independently through extensive data training. A practical example involves recognizing handwritten digits. Each digit is represented as a pixel grid processed by neurons in a neural network. The input layer receives pixel data, transferring information through hidden layers to the output layer, where each neuron corresponds to a digit. Connections between neurons have weighted values and biases, affecting how information flows through the network. Activation functions determine whether neurons activate based on input sums, adjusting weights and biases continuously to train the network effectively. Deep learning finds applications in various domains, including customer support, where automated systems can engage in conversations, illustrating its capabilities in real-world tasks.