jeffheaton/app_deep_learning
A university course teaching deep neural network architectures including CNNs, LSTMs, and GANs with PyTorch for applications in computer vision, NLP, and time series.
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T81-558 is a Washington University course that teaches deep learning through practical programming assignments. The course covers classic neural networks, convolutional networks, recurrent networks, and generative adversarial networks applied to computer vision, time series, NLP, and data generation. Students implement deep learning models using Python and PyTorch, with optional coverage of GPU and high-performance computing aspects.
Frequently asked
- What is jeffheaton/app_deep_learning?
- A university course teaching deep neural network architectures including CNNs, LSTMs, and GANs with PyTorch for applications in computer vision, NLP, and time series.
- Is app_deep_learning open source?
- Yes — jeffheaton/app_deep_learning is open source, released under the Apache-2.0 license.
- What language is app_deep_learning written in?
- jeffheaton/app_deep_learning is primarily written in Jupyter Notebook.
- How popular is app_deep_learning?
- jeffheaton/app_deep_learning has 492 stars on GitHub.
- Where can I find app_deep_learning?
- jeffheaton/app_deep_learning is on GitHub at https://github.com/jeffheaton/app_deep_learning.