Deep Learning

Deep learning is a branch of machine learning that uses multilayer neural networks, also known as artificial neural networks (ANNs). These networks are inspired by the way biological nervous systems process information and can model complex relationships in data.

Deep-learning models can work with unstructured data and learn useful features and relationships without requiring every example to be labeled manually. This ability often improves the quality of predictions made from raw data.

Deep learning is used across data science to automate tasks, analyze performance, and support applications such as digital assistants, voice-enabled devices, credit-card fraud detection, self-driving cars, and generative AI.

• Data enters the model (input layer)

  The input layer receives the dataset used by the model. Unlike many traditional machine-learning workflows, deep-learning models can work directly with relatively raw data, although preprocessing is still often useful.

• The neural network analyzes the data (hidden layers)

  Each neuron applies a weight that represents the importance of a signal, then performs mathematical operations to identify patterns. More hidden layers allow the model to represent increasingly complex relationships.

• The model produces a decision (output layer)

  The output layer assigns a probability to each possible answer. The model returns the class or value with the highest probability as its output.