neural-network-papers
Tech stack & purpose
neural-network-papers is an awesome-list style repository that serves as a curated catalog of papers, resources, and references related to neural networks and deep learning. The repository organizes content across numerous domains including natural language processing, convolutional and recurrent neural networks, reinforcement learning, adversarial networks, autoencoders, and theoretical foundations, alongside practical resources like datasets, pretrained models, and programming frameworks. The tech stack covered includes frameworks such as TensorFlow, Caffe, Theano, Torch, and Deeplearning4j, as well as supporting libraries like NumPy and cuDNN. While the repository itself does not appear to have a single attributed author or organization, it compiles work from numerous researchers and institutions, referencing contributions from figures like Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, as well as labs such as the Montreal Institute for Learning Algorithms and the Berkeley Vision and Learning Center.
Community & reference links
Languages
Contributors
- Robert Dionne210 commits
- Robert Dionne5 commits · 152 followers
- Fengbin Tu1 commits
