Autoencoders are artificial neural networks used to learn efficient representations of unlabeled data. They learn an encoding and decoding function, which can be used for dimensionality reduction or generative modeling. Autoencoders have been applied to a variety of tasks such as facial recognition, feature detection, anomaly detection, and natural language processing.

UC Berkeley

Fall 2022

An advanced course dealing with deep networks in the fields of computer vision, language technology, robotics, and control. It delves into the themes of deep learning, model families, and real-world applications. A strong mathematical background in calculus, linear algebra, probability, optimization, and statistical learning is necessary.

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+ 14 more conceptsCarnegie Mellon University

Spring 2020

This course provides a comprehensive introduction to deep learning, starting from foundational concepts and moving towards complex topics such as sequence-to-sequence models. Students gain hands-on experience with PyTorch and can fine-tune models through practical assignments. A basic understanding of calculus, linear algebra, and Python programming is required.

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+ 40 more conceptsBrown University

Spring 2022

Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts.

No concepts data

+ 40 more concepts