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Deep learning uses multilayer neural networks to learn representations and solve tasks like classification and regression.
Deep learning is a branch of machine learning that uses multilayered neural networks to learn from data and perform tasks such as classification, regression, and representation learning. It is inspired by biological neuroscience, using stacked “artificial neurons” arranged in layers and trained to transform inputs into useful internal representations. The core idea is that “deep” refers to the number of layers (often from a few to hundreds or thousands) through which data is transformed. As an example in image recognition, early layers can learn simple features (like edges), while later layers build more complex concepts (like faces). Unlike earlier approaches that relied heavily on hand-crafted feature engineering, deep learning can automatically discover which features to extract and at what level, using supervised, semi-supervised, or unsupervised training methods.
Deep learning uses multilayer neural networks to learn representations and solve tasks like classification and regression.
The “deep” in deep learning refers to many layers, enabling a hierarchy of increasingly abstract feature representations.
Deep learning reduces the need for hand-crafted features by learning useful feature representations directly from data.
Training can be supervised, semi-supervised, or unsupervised, and many architectures (e.g., CNNs, transformers) support different application needs.
A machine learning approach that uses multilayer neural networks to transform inputs into progressively more abstract representations for tasks like prediction and recognition.
Learning internal feature representations from data that improve performance on downstream tasks.
The depth of the transformation chain from input to output in a network, reflecting how many layers contribute to producing the final result.
Manually designing transformations to extract useful features before training a model, which deep learning often replaces with learned features.
Training where the model learns from labeled input-output pairs.
Training where the model learns patterns from unlabeled data, often to discover useful representations.
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