{"id":18074425,"url":"https://github.com/zellyn/deeplearning-class-2011","last_synced_at":"2025-10-16T03:02:17.509Z","repository":{"id":1525995,"uuid":"1796059","full_name":"zellyn/deeplearning-class-2011","owner":"zellyn","description":"Code for Deep Learning class at Google","archived":false,"fork":false,"pushed_at":"2011-09-01T16:47:42.000Z","size":55114,"stargazers_count":73,"open_issues_count":0,"forks_count":68,"subscribers_count":11,"default_branch":"master","last_synced_at":"2025-07-25T04:55:59.337Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Matlab","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/zellyn.png","metadata":{"files":{"readme":"README.org","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}},"created_at":"2011-05-24T22:18:28.000Z","updated_at":"2023-08-07T02:49:24.000Z","dependencies_parsed_at":"2022-08-16T13:35:22.476Z","dependency_job_id":null,"html_url":"https://github.com/zellyn/deeplearning-class-2011","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/zellyn/deeplearning-class-2011","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zellyn%2Fdeeplearning-class-2011","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zellyn%2Fdeeplearning-class-2011/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zellyn%2Fdeeplearning-class-2011/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zellyn%2Fdeeplearning-class-2011/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zellyn","download_url":"https://codeload.github.com/zellyn/deeplearning-class-2011/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zellyn%2Fdeeplearning-class-2011/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268197322,"owners_count":24211675,"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","status":"online","status_checked_at":"2025-08-01T02:00:08.611Z","response_time":67,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2024-10-31T10:12:37.812Z","updated_at":"2025-10-16T03:02:17.404Z","avatar_url":"https://github.com/zellyn.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"* Overview\nMy code for the Deep Learning class exercises. There should be nothing\nproprietary in here. For the earlier exercises, I tried to create\nparallel implementations in Octave and NumPy. Later on, class-supplied\nhelper code necessitated the use of Matlab (for now).\n\n* Materials\n- [[http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning][OpenClassroom Regression Tutorial]]\n- [[http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial][UFLDF Tutorial Wiki]]\n\n* Note\n\n- The L-BFGS Matlab code is licensed by Stanford under a Creative Commons,\n  Attribute, Non-Commercial license. Please read the\n  [[http://ufldl.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder#Sparse_autoencoder_implementation][details on the UFLDL wiki]].\n- The MNIST digit data comes from [[http://yann.lecun.com/exdb/mnist/]].\n* Questions\n** UFLDL\n\"Since J(W,b) is a non-convex function, gradient descent is\nsusceptible to local optima; however, in practice gradient descent\nusually works fairly well.\" - [[http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm][UFLDL/Backpropagation]]\n\nWhy? Is it *almost* convex? Are the local optima all of a similar\nquality? Are any of the variations (squared error / squared error +\nweight decay / squared error + weight decay + sparsity constraints)\nconvex?\n* Tasks\n** Python\n*** TODO Get [[file:ufldf/stackedae_exercise/stackedae_exercise.py][stackedae_exercise.py]] to work.\n*** TODO Implement [[file:ufldf/linear_decoder_exercise][linear_decoder_exercise]].\n*** TODO Implement [[file:ufldf/cnn_exercise][cnn_exercise]].\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzellyn%2Fdeeplearning-class-2011","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzellyn%2Fdeeplearning-class-2011","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzellyn%2Fdeeplearning-class-2011/lists"}