{"id":18745560,"url":"https://github.com/vpanjeta/deep-learning-models","last_synced_at":"2025-04-12T21:32:53.907Z","repository":{"id":216017202,"uuid":"77289953","full_name":"VPanjeta/Deep-Learning-Models","owner":"VPanjeta","description":"Deep Learning Models implemented in python.","archived":false,"fork":false,"pushed_at":"2017-03-19T14:56:25.000Z","size":30,"stargazers_count":17,"open_issues_count":0,"forks_count":12,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-26T16:01:41.033Z","etag":null,"topics":["cnn","da","deep-belief-nets","deep-learning","deeplearning","denoising-autoencoders","mlp","models","multi-layer-perceptron","python","rbm","restricted-boltzmann-machine","sda"],"latest_commit_sha":null,"homepage":null,"language":"Python","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/VPanjeta.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}},"created_at":"2016-12-24T15:50:55.000Z","updated_at":"2023-08-23T12:58:20.000Z","dependencies_parsed_at":"2024-01-08T02:11:03.210Z","dependency_job_id":"bc994166-81fe-4c56-a753-6a04861458bf","html_url":"https://github.com/VPanjeta/Deep-Learning-Models","commit_stats":null,"previous_names":["vpanjeta/deep-learning-models"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VPanjeta%2FDeep-Learning-Models","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VPanjeta%2FDeep-Learning-Models/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VPanjeta%2FDeep-Learning-Models/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VPanjeta%2FDeep-Learning-Models/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VPanjeta","download_url":"https://codeload.github.com/VPanjeta/Deep-Learning-Models/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248636332,"owners_count":21137430,"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":["cnn","da","deep-belief-nets","deep-learning","deeplearning","denoising-autoencoders","mlp","models","multi-layer-perceptron","python","rbm","restricted-boltzmann-machine","sda"],"created_at":"2024-11-07T16:18:39.500Z","updated_at":"2025-04-12T21:32:53.010Z","avatar_url":"https://github.com/VPanjeta.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep-Learning-Models\nDeep Learning Models implemented in python using numpy arrays. \u003cbr/\u003e\nInspired by Deep learning repository https://github.com/yusugomori/DeepLearning/tree/master/cpp \u003cbr/\u003e\n\nFiles added: \u003cbr/\u003e\n1. fucntions.py - Contains the necessary functions for the models. It will be updated frequently as the functions are used in the uploaded files.\u003cbr/\u003e\n2.  RBM.py      - Restricted Boltzmann Machine. (A Boltzmann Machine with 2 bipartite layers (visible and hidden)\u003cbr/\u003e\n3.  HL.py       - Hidden Layer : The layers above the input layers. \u003cbr/\u003e\n4.  LR.py       - Logistic regression class.\u003cbr/\u003e\n5.  DBN.py      - Deep Belief Nets, A multi layer Restricted Boltzmann Machine.\u003cbr/\u003e\n6.  CRBM.py     - Restricted Boltzmann Machine with continuous valued-inputs. Extends the RBM to capture temporal dependencies. \u003cbr/\u003e\n7.  CDBN.py     - Deep Belief Nets with continued value points input. \u003cbr/\u003e\n8.  MLP.py      - Multi Layer Perceptron.\u003cbr/\u003e\n9.  dA.py       - Denoising Autoencoder.\u003cbr/\u003e\n10. SdA.py      - Stacked denoising Autoencoders. \u003cbr/\u003e\n11. CPL.py  \t- Convolution and Max Pooling.\u003cbr/\u003e\n12. CNN.py \t\t- Convolutional Neural Network.\u003cbr/\u003e\n13. SVM.py  \t- Support Vector Machine. \u003cbr/\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvpanjeta%2Fdeep-learning-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvpanjeta%2Fdeep-learning-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvpanjeta%2Fdeep-learning-models/lists"}