{"id":24881734,"url":"https://github.com/chaitanyac22/neural-network-using-numpy","last_synced_at":"2026-04-20T13:31:30.818Z","repository":{"id":154355869,"uuid":"346803580","full_name":"ChaitanyaC22/Neural-Network-using-Numpy","owner":"ChaitanyaC22","description":"Introduction to Neural Networks (Create a neural network using Numpy)","archived":false,"fork":false,"pushed_at":"2021-07-09T18:28:42.000Z","size":16619,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"chai_main","last_synced_at":"2025-06-14T15:03:36.301Z","etag":null,"topics":["accuracy","activation-functions","backpropagation-learning-algorithm","feed-forward","loss","mnist-classification","model","neural-networks","neural-networks-from-scratch","numpy","prediction","python3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ChaitanyaC22.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2021-03-11T18:45:08.000Z","updated_at":"2025-02-15T06:10:59.000Z","dependencies_parsed_at":null,"dependency_job_id":"7aa40d5c-286d-40f5-a68e-d0be88cd3ce5","html_url":"https://github.com/ChaitanyaC22/Neural-Network-using-Numpy","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ChaitanyaC22/Neural-Network-using-Numpy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChaitanyaC22%2FNeural-Network-using-Numpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChaitanyaC22%2FNeural-Network-using-Numpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChaitanyaC22%2FNeural-Network-using-Numpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChaitanyaC22%2FNeural-Network-using-Numpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ChaitanyaC22","download_url":"https://codeload.github.com/ChaitanyaC22/Neural-Network-using-Numpy/tar.gz/refs/heads/chai_main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChaitanyaC22%2FNeural-Network-using-Numpy/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32048930,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-20T11:35:06.609Z","status":"ssl_error","status_checked_at":"2026-04-20T11:34:48.899Z","response_time":94,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["accuracy","activation-functions","backpropagation-learning-algorithm","feed-forward","loss","mnist-classification","model","neural-networks","neural-networks-from-scratch","numpy","prediction","python3"],"created_at":"2025-02-01T12:13:23.618Z","updated_at":"2026-04-20T13:31:30.801Z","avatar_url":"https://github.com/ChaitanyaC22.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural-Network-using-Numpy\nIntroduction to Neural Networks (Create a neural network using Numpy)\n\nIn this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, loss computation, backpropagation, weight updates etc.\n\nYou will use the MNIST dataset to train your model to classify handwritten digits between 0-9.\n\nThe assignment is divided into the following sections:\n\n-   Data preparation\n-   Feedforward\n-   Loss computation\n-   Backpropagation\n-   Parameter updates\n-   Model training and predictions\n\nFor Ipython notebook: (Refer to Neural-Networks-using-Numpy.ipynb file)\n\nFor  the dataset: (Refer to mnist.pkl.gz file)\n\n## Jupyter Notebook Viewer\nIf you are unable to view or load the jupyter IPython notebook via Github, please click on this [link](https://nbviewer.jupyter.org/github/ChaitanyaC22/Neural-Network-using-Numpy/blob/chai_main/Neural-Network-using-Numpy.ipynb).\nThank you!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchaitanyac22%2Fneural-network-using-numpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchaitanyac22%2Fneural-network-using-numpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchaitanyac22%2Fneural-network-using-numpy/lists"}