How do machine learning and deep learning models work?

Machine learning and deep learning models are based on the concept of artificial neural networks. These models learn from data by identifying patterns and relationships. Machine learning models work by training on labeled datasets, extracting relevant features, and then using statistical algorithms to make predictions or classify new instances. Deep learning models, on the other hand, utilize complex neural architectures with multiple hidden layers which enable them to automatically learn hierarchical representations of data. These models are trained using large amounts of unlabeled data, allowing them to gradually build a hierarchy of increasingly abstract concepts. Deep learning models excel in tasks like image recognition, natural language processing, and speech recognition. Both machine learning and deep learning models work by iteratively improving their predictions through optimization algorithms, which adjust their internal parameters to minimize errors.

This mind map was published on 28 August 2023 and has been viewed 71 times.