{"id":15420068,"url":"https://github.com/hhhhhhao/mlpractical","last_synced_at":"2025-10-27T10:11:35.750Z","repository":{"id":106726044,"uuid":"149993408","full_name":"Hhhhhhao/mlpractical","owner":"Hhhhhhao","description":"Machine Learning Practical (2018-2019)","archived":false,"fork":false,"pushed_at":"2018-12-15T20:30:18.000Z","size":265012,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"mlp2018-9/lab1","last_synced_at":"2025-02-09T12:19:46.297Z","etag":null,"topics":[],"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/Hhhhhhao.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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-09-23T14:43:40.000Z","updated_at":"2019-01-13T08:21:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"6f8de62e-765d-4aa8-b6e7-7beb19fcb751","html_url":"https://github.com/Hhhhhhao/mlpractical","commit_stats":{"total_commits":336,"total_committers":13,"mean_commits":"25.846153846153847","dds":0.5833333333333333,"last_synced_commit":"8beb48fcb27c6655287080bdc91c390e1c3e47e9"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hhhhhhao%2Fmlpractical","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hhhhhhao%2Fmlpractical/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hhhhhhao%2Fmlpractical/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Hhhhhhao%2Fmlpractical/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Hhhhhhao","download_url":"https://codeload.github.com/Hhhhhhao/mlpractical/tar.gz/refs/heads/mlp2018-9/lab1","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247099472,"owners_count":20883402,"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":[],"created_at":"2024-10-01T17:28:10.596Z","updated_at":"2025-10-27T10:11:35.690Z","avatar_url":"https://github.com/Hhhhhhao.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Practical\n\nThis repository contains the code for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course [Machine Learning Practical](http://www.inf.ed.ac.uk/teaching/courses/mlp/).\n\nThis assignment-based course is focused on the implementation and evaluation of machine learning systems. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.\n\nThe code in this repository is split into:\n\n  *  a Python package `mlp`, a [NumPy](http://www.numpy.org/) based neural network package designed specifically for the course that students will implement parts of and extend during the course labs and assignments,\n  *  a series of [Jupyter](http://jupyter.org/) notebooks in the `notebooks` directory containing explanatory material and coding exercises to be completed during the course labs.\n\n## Getting set up\n\nDetailed instructions for setting up a development environment for the course are given in [this file](notes/environment-set-up.md). Students doing the course will spend part of the first lab getting their own environment set up.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhhhhhhao%2Fmlpractical","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhhhhhhao%2Fmlpractical","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhhhhhhao%2Fmlpractical/lists"}