{"id":20556519,"url":"https://github.com/somjit101/mnist-classification-keras","last_synced_at":"2026-04-16T04:31:34.012Z","repository":{"id":179926694,"uuid":"406909615","full_name":"somjit101/MNIST-Classification-Keras","owner":"somjit101","description":"A simple study on the use of Keras framework (with Tensorflow background) for a simple handwritten number image classification task with Deep Neural Networks.","archived":false,"fork":false,"pushed_at":"2021-09-17T19:20:47.000Z","size":581,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-14T17:03:47.897Z","etag":null,"topics":["adam-optimizer","batch-normalization","deep-learning","dropout-keras","grid-search","hyperparameter-tuning","image-classification","keras","mnist","mnist-classification","neural-network","sgd-optimizer","tensorflow"],"latest_commit_sha":null,"homepage":"","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/somjit101.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":"2021-09-15T20:03:32.000Z","updated_at":"2022-06-27T10:24:09.000Z","dependencies_parsed_at":null,"dependency_job_id":"05df4961-fea1-46cc-b30b-28c80559647a","html_url":"https://github.com/somjit101/MNIST-Classification-Keras","commit_stats":null,"previous_names":["somjit101/mnist-classification-keras"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/somjit101/MNIST-Classification-Keras","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/somjit101%2FMNIST-Classification-Keras","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/somjit101%2FMNIST-Classification-Keras/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/somjit101%2FMNIST-Classification-Keras/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/somjit101%2FMNIST-Classification-Keras/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/somjit101","download_url":"https://codeload.github.com/somjit101/MNIST-Classification-Keras/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/somjit101%2FMNIST-Classification-Keras/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31871447,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"online","status_checked_at":"2026-04-16T02:00:06.042Z","response_time":69,"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":["adam-optimizer","batch-normalization","deep-learning","dropout-keras","grid-search","hyperparameter-tuning","image-classification","keras","mnist","mnist-classification","neural-network","sgd-optimizer","tensorflow"],"created_at":"2024-11-16T03:28:43.323Z","updated_at":"2026-04-16T04:31:33.992Z","avatar_url":"https://github.com/somjit101.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MNIST-Classification-Keras\n\nA simple, exploratory study on the use of Deep Neural Networks (DNNs) with Keras framework (Tensorflow background) for a simple handwritten number image classification task. This project was primarily made with the purpose of learning and getting familiar with Multi-layered Perceptrons, training and performance testing in Keras framework, which efficiently streamlines its implementation with intuitive, simple-to-use functional APIs. This eliminates the need of managing computational graphs in Tensorflow and allows us to easily play with the Neural Network Architecture.\n\n## Dataset \n\nWe have used the renowned [MNIST Handwritten Digits Dataset](http://yann.lecun.com/exdb/mnist/) containing 60,000 train samples and 10,000 test samples of 28x28 grayscale images depicting numerical digits written by a huge number of human subjects. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsomjit101%2Fmnist-classification-keras","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsomjit101%2Fmnist-classification-keras","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsomjit101%2Fmnist-classification-keras/lists"}