{"id":19662669,"url":"https://github.com/sweetpand/pytorch_fun","last_synced_at":"2025-04-28T21:32:08.652Z","repository":{"id":129588819,"uuid":"217877990","full_name":"sweetpand/PyTorch_fun","owner":"sweetpand","description":"Logo Detection model with PyTorch by using keras and Flask","archived":false,"fork":false,"pushed_at":"2020-04-16T09:19:43.000Z","size":2319,"stargazers_count":5,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-05T11:12:55.476Z","etag":null,"topics":["flask","logo-detection","logo-detection-model","pythorch"],"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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sweetpand.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}},"created_at":"2019-10-27T15:48:52.000Z","updated_at":"2024-09-30T16:34:30.000Z","dependencies_parsed_at":"2023-06-06T07:45:50.524Z","dependency_job_id":null,"html_url":"https://github.com/sweetpand/PyTorch_fun","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sweetpand%2FPyTorch_fun","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sweetpand%2FPyTorch_fun/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sweetpand%2FPyTorch_fun/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sweetpand%2FPyTorch_fun/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sweetpand","download_url":"https://codeload.github.com/sweetpand/PyTorch_fun/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251391263,"owners_count":21582138,"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":["flask","logo-detection","logo-detection-model","pythorch"],"created_at":"2024-11-11T16:12:04.295Z","updated_at":"2025-04-28T21:32:08.639Z","avatar_url":"https://github.com/sweetpand.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Logo_detection_PyTorch\nLogo Detection model with PyTorch by using keras and Flask\n\nhttps://heartbeat.fritz.ai/logo-recognition-ios-application-using-machine-learning-and-flask-api-aec4eff3be11\n\n\n# PyTorch Notebooks\nA collection of PyTorch notebooks for studying and practicing deep learning. Each notebook contains a set of exercises that are specifically designed to engage and encourage the learner to conduct more research and experiments. (Work in progress!)\n\n\u003ctable class=\"tg\"\u003e\n  \u003ctr\u003e\n    \u003cth class=\"tg-yw4l\"\u003e\u003cb\u003eName\u003c/b\u003e\u003c/th\u003e\n    \u003cth class=\"tg-yw4l\"\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/th\u003e\n    \u003cth class=\"tg-yw4l\"\u003e\u003cb\u003eCategory\u003c/b\u003e\u003c/th\u003e\n    \u003cth class=\"tg-yw41\"\u003e\u003cb\u003eLevel\u003c/b\u003e\u003c/th\u003e\n    \u003cth class=\"tg-yw4l\"\u003e\u003cb\u003eLink \u003c/b\u003e\u003c/th\u003e\n    \u003cth class=\"tg-yw4l\"\u003e\u003cb\u003eBlog \u003c/b\u003e\u003c/th\u003e\n    \n  \u003c/tr\u003e\n  \n  \u003ctr\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eImplementing a Logistic Regression Model from Scratch\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eLearn how to implement the fundamental building blocks of a neural network using PyTorch.\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eMachine Learning\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eBeginner\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://colab.research.google.com/drive/1AWXvwvyoOczCugTTULLbIPYIh2_GS2Aq\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" width = '400px' \u003e\n\u003c/a\u003e\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://medium.com/dair-ai/implementing-a-logistic-regression-model-from-scratch-with-pytorch-24ea062cd856?source=friends_link\u0026sk=49dcddb17d1d021d2d677f3666c884636\"\u003eread\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \n  \n  \u003ctr\u003e\n    \u003ctd class=\"tg-yw4l\"\u003ePyTorch Hello World\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eCreate a hello world for deep learning using PyTorch.\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eDeep Learning\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eBeginner\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://colab.research.google.com/drive/1ac0K9_aa46c77XEeYtaMAfSOfmH1Bl9L\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" width = '400px' \u003e\n\u003c/a\u003e\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://medium.com/dair-ai/a-first-shot-at-deep-learning-with-pytorch-4a8252d30c75?sk=729868741e9809dc3bba6e28a4d7af10\"\u003eread\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \n  \u003ctr\u003e\n    \u003ctd class=\"tg-yw4l\"\u003ePyTorch Quickstart\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eLearn about PyTorch's basic building blocks to build and train a CNN model for image classification.\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eImage Classification\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eIntermediate\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://colab.research.google.com/github/omarsar/pytorch_notebooks/blob/master/pytorch_quick_start.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" width = '400px' \u003e\n\u003c/a\u003e\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d\"\u003eread\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eA Gentle Introduction to PyTorch 1.2\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eThis comprehensive tutorial aims to introduce the fundamentals of PyTorch building blocks for training neural networks.\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eNeural Networks\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003eBeginner\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://github.com/omarsar/pytorch_notebooks/blob/master/A_Gentle_Introduction_to_PyTorch_1_2.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" width = '400px' \u003e\n\u003c/a\u003e\u003c/td\u003e\n    \u003ctd class=\"tg-yw4l\"\u003e\u003ca href=\"https://medium.com/dair-ai/pytorch-1-2-introduction-guide-f6fa9bb7597c\"\u003eread\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \n  \n  \n  \n\u003c/table\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsweetpand%2Fpytorch_fun","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsweetpand%2Fpytorch_fun","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsweetpand%2Fpytorch_fun/lists"}