{"id":22546694,"url":"https://github.com/u66u/lotus","last_synced_at":"2025-06-24T07:08:45.667Z","repository":{"id":196111442,"uuid":"694249518","full_name":"u66u/lotus","owner":"u66u","description":"ML framework for simple neural networks with optimal defaults","archived":false,"fork":false,"pushed_at":"2023-10-10T17:01:47.000Z","size":64,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-02T09:31:15.675Z","etag":null,"topics":["ai","artificial-intelligence","cpp","learning-resources","machine-learning","ml","neural-network","neural-networks","open-source","pytorch","tensorflow","tf","torch","transformers","tutorial"],"latest_commit_sha":null,"homepage":"","language":"C++","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/u66u.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}},"created_at":"2023-09-20T16:06:32.000Z","updated_at":"2023-10-10T17:45:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"c49be730-5c90-4765-886a-ff031fea165d","html_url":"https://github.com/u66u/lotus","commit_stats":null,"previous_names":["technicca/lotus","tplx/lotus","u66u/lotus"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/u66u%2Flotus","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/u66u%2Flotus/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/u66u%2Flotus/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/u66u%2Flotus/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/u66u","download_url":"https://codeload.github.com/u66u/lotus/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245999319,"owners_count":20707554,"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":["ai","artificial-intelligence","cpp","learning-resources","machine-learning","ml","neural-network","neural-networks","open-source","pytorch","tensorflow","tf","torch","transformers","tutorial"],"created_at":"2024-12-07T15:08:35.712Z","updated_at":"2025-03-28T08:46:02.599Z","avatar_url":"https://github.com/u66u.png","language":"C++","readme":"set different ranges for different weght\nbias inits, set default ranges for init functions\ntest memory\ncheck iterators in layer\n\n\nImplement differen weight/bias init for xavier:\n\n\nThe original paper by Glorot and Bengio suggests a variance of 2/(n_in + n_out) where n_in is the number of inputs and n_out is the number of outputs of the neuron 365datascience.com.\n\nSome sources, such as the deeplearning.ai notes, suggest a variance of 1/n_in deeplearning.ai.\n\nSome other sources, such as machinelearningmastery.com, suggest a uniform distribution in the range -(sqrt(6)/sqrt(n_in + n_out)) to sqrt(6)/sqrt(n_in + n_out) machinelearningmastery.com.\n\nprint statements are triggered even if the ranges are defined for init methods","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fu66u%2Flotus","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fu66u%2Flotus","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fu66u%2Flotus/lists"}