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Deep learning uses multilayer neural networks to learn hierarchical, increasingly abstract representations from data.
Deep learning (DL) is a branch of machine learning that uses multilayered neural networks to transform input data into progressively more abstract and useful representations for tasks such as classification, regression, and representation learning. The “deep” in deep learning refers to the use of many layers (often from a few to hundreds or thousands), which creates a long chain of transformations from input to output (often described in terms of credit assignment path depth). Deep learning models can be trained in supervised, semi-supervised, or unsupervised ways, and they learn useful features automatically rather than relying on hand-crafted feature engineering. In terms of scope, deep learning covers a wide range of neural network architectures (e.g., convolutional neural networks, recurrent neural networks, transformers, and generative models) and is applied across many domains. The content highlights major application areas such as computer vision (image recognition), speech recognition, natural language processing and machine translation, bioinformatics and drug design, medical image analysis, and other fields like climate science, material inspection, and fraud detection. While early neural networks were inspired by biological neuroscience, modern deep learning generally does not aim to model the brain directly; instead, it focuses on learning effective representations from data.
Deep learning uses multilayer neural networks to learn hierarchical, increasingly abstract representations from data.
The term “deep” refers to the number of layers/credit assignment path depth, enabling feature learning without hand-crafted features.
Deep learning spans multiple training paradigms (supervised, semi-supervised, unsupervised) and many architectures (e.g., CNNs, RNNs, transformers, generative models).
Its scope includes broad real-world applications such as speech recognition, image recognition, natural language processing, and bio/medical and other scientific and commercial domains.
A class of machine learning algorithms that uses hierarchical multilayer neural networks to transform inputs into abstract representations for tasks like classification and regression.
The depth of the chain of transformations from input to output in a neural network, used to characterize how “deep” a model is.
Manually designing transformations of data into features for a model to use; deep learning reduces the need for this by learning features automatically.
Training where models learn from labeled input-output pairs.
Training where models learn patterns from unlabeled data, often to discover useful representations.
A neural network architecture designed to process data with grid-like structure (e.g., images) using convolutional layers.
A neural network architecture for sequence data where information can propagate through time, often unrolled into a deep feedforward-like computation graph.
A modern neural network architecture commonly used for sequence modeling, especially in natural language processing.
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