{"id":15631266,"url":"https://github.com/pppw/deep-learning-random-explore","last_synced_at":"2025-09-03T02:35:15.147Z","repository":{"id":93596677,"uuid":"153917356","full_name":"PPPW/deep-learning-random-explore","owner":"PPPW","description":null,"archived":false,"fork":false,"pushed_at":"2019-08-09T03:02:15.000Z","size":3721,"stargazers_count":194,"open_issues_count":3,"forks_count":34,"subscribers_count":17,"default_branch":"master","last_synced_at":"2024-11-27T13:21:46.732Z","etag":null,"topics":["backward-propagation","cnn-architecture","deep-learning","fastai","keras","lstm"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PPPW.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-10-20T15:02:55.000Z","updated_at":"2024-08-22T09:21:38.000Z","dependencies_parsed_at":null,"dependency_job_id":"4ff7ca0a-6d83-4807-9926-2e33e6e91413","html_url":"https://github.com/PPPW/deep-learning-random-explore","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PPPW%2Fdeep-learning-random-explore","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PPPW%2Fdeep-learning-random-explore/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PPPW%2Fdeep-learning-random-explore/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PPPW%2Fdeep-learning-random-explore/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PPPW","download_url":"https://codeload.github.com/PPPW/deep-learning-random-explore/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230532450,"owners_count":18240792,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["backward-propagation","cnn-architecture","deep-learning","fastai","keras","lstm"],"created_at":"2024-10-03T10:39:45.305Z","updated_at":"2024-12-20T04:08:10.947Z","avatar_url":"https://github.com/PPPW.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Learning: Random Explore\n\nA set of notebooks explore deep learning related topics. \n\n* [CNN architectures](CNN_archs/cnn_archs.ipynb): look into the structures of common CNN architectures, such as ResNet, [ResNeXt](https://arxiv.org/abs/1611.05431), [SENet](https://arxiv.org/pdf/1709.01507.pdf), [Densenet](https://arxiv.org/pdf/1608.06993.pdf), [Inception V4](https://arxiv.org/pdf/1602.07261.pdf), [WRN](https://arxiv.org/pdf/1605.07146.pdf), [Xception](https://arxiv.org/pdf/1610.02357.pdf), [Dual Path Networks](https://arxiv.org/abs/1707.01629), [NASNet](https://arxiv.org/abs/1707.07012), [Progressive Neural Architecture Search](https://arxiv.org/abs/1712.00559), [VGG](https://arxiv.org/pdf/1409.1556.pdf), etc., and how to use them in [fastai](https://docs.fast.ai/).\n\n* [EfficientNet paper study](efficientnet/EfficientNet.ipynb): study the official implementation. As the building blocks, the mobile inverted residual blocks and the Squeeze-and-Excitation networks are also studied here.\n\n* [WGAN paper study](wgan/wgan.ipynb): replicate some results in the WGAN paper. \n\n* [An easy way to do the backward propagation math](backward_propagation_for_all/README.md): use a simple rule to derive the backward propagation for all different kinds of neural networks, such as LSTM, CNN, etc. \n\n* [Resume interrupted 1cycle policy training](divide_1cycle/README.md): divide the long training process into smaller ones and resume the training. \n\n* [How the LSTM's memory works?](LSTM_memory_cells/README.md): dig into the LSTM's internal states to see how it manages to generate valid XML texts. \n\n* [Parameter counts for popular CNN architectures](CNN_archs_param_counts/README.md)\n\n* _To be continued ..._\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpppw%2Fdeep-learning-random-explore","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpppw%2Fdeep-learning-random-explore","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpppw%2Fdeep-learning-random-explore/lists"}