Deep learning is a branch of machine learning that relies on multi-layered artificial neural networks to learn and extract features from data. Unlike conventional machine learning, which relies on algorithms that require features to be defined manually, deep learning allows models to learn autonomously from raw data.
The artificial neural networks used in deep learning are composed of multiple layers of interconnected neurons, each of which processes a portion of the data and the extracted features are used in subsequent layers to extract more complex features. This process is repeated at each layer until the most abstract features are extracted from the dataset.
Deep learning is used in a variety of applications, such as computer vision, natural language processing, machine translation, fraud detection, object identification, among others. Due to its ability to learn autonomously and its high accuracy in identifying complex patterns in data, deep learning has become a powerful tool in the field of artificial intelligence and machine learning.
Reference: Yoshua Bengio. Learning Deep Architectures for AI.
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