Deep learning, a subset of machine learning and artificial intelligence, enables machines to mimic human behavior through advanced algorithms and data training. Unlike traditional machine learning that requires humans to define features for classification, such as differentiating tomatoes and cherries based on size and shape, deep learning uses artificial neural networks to autonomously extract features from data, necessitating larger datasets for training. For instance, in digit recognition, a neural network processes an image of a digit composed of 28 by 28 pixels, totaling 784 inputs. Each pixel corresponds to a neuron in the input layer, while the output layer represents the recognized digit. Between these layers are hidden layers where information is processed through weighted channels. Each neuron has a unique bias, which is added to the weighted sum of inputs, subsequently passed through an activation function that determines if the neuron is activated. This process continues until the final output layer, where the activated neuron indicates the recognized digit. Through continuous adjustments of weights and biases, the network learns to improve its accuracy. Deep learning's applications extend to various fields, including customer support systems, where it enhances interactions by enabling more effective communication between users and automated agents.