What is Deep Learning?
Also known as: DL
Deep Learning is a subset of machine learning that uses multi-layered artificial neural networks to automatically learn complex patterns from large datasets.
It extends traditional neural networks by adding many hidden layers between input and output, enabling the model to learn hierarchical representations of data where early layers capture simple features and deeper layers combine them into more abstract concepts.
Training relies on algorithms like backpropagation and gradient descent, often accelerated by GPUs, to adjust millions of parameters using massive amounts of labeled data.
This approach allows systems to improve performance on tasks like perception and prediction without manual feature engineering.
Example
A deep learning model trained on millions of photos can identify cats by first detecting edges and textures in early layers, then shapes like ears and whiskers in middle layers, and finally recognizing complete cat faces in deeper layers.
Why it matters
Deep learning drives most state-of-the-art results in computer vision, speech recognition, and natural language processing, powering practical AI applications used daily by billions of people.
Frequently asked questions
It refers to the large number of layers in the neural network, which allows the model to learn increasingly complex features automatically.
Related terms
A neural network, or artificial neural network (ANN), is a computational model inspired by the human brain that learns to recognize patterns in data by passing information through layers of interconnected artificial neurons.
Machine learning is a branch of artificial intelligence in which algorithms learn patterns from data to make predictions or decisions, rather than following hand-coded rules for every situation.
A Convolutional Neural Network (CNN) is a specialized type of deep neural network designed to process grid-like data such as images by automatically learning spatial patterns and features.
Backpropagation is an algorithm for training neural networks by calculating how much each weight contributed to the prediction error and adjusting those weights accordingly. It uses the chain rule to efficiently compute gradients of the loss function.
Artificial Intelligence (AI) is the field of computer science focused on creating machines that can perform tasks typically requiring human intelligence, such as learning, reasoning, and decision-making.
A Recurrent Neural Network (RNN) is a type of neural network built to handle sequential data by passing information from one step to the next through a hidden state that acts like a memory.