{"id":13936736,"url":"https://github.com/gabrieleangeletti/Deep-Learning-TensorFlow","last_synced_at":"2025-07-19T22:31:45.937Z","repository":{"id":36566505,"uuid":"40872421","full_name":"gabrieleangeletti/Deep-Learning-TensorFlow","owner":"gabrieleangeletti","description":"Ready to use implementations of various Deep Learning algorithms using TensorFlow.","archived":false,"fork":false,"pushed_at":"2017-09-24T15:52:26.000Z","size":18435,"stargazers_count":965,"open_issues_count":25,"forks_count":376,"subscribers_count":91,"default_branch":"master","last_synced_at":"2024-11-15T07:42:17.744Z","etag":null,"topics":["deep-learning","tensorflow"],"latest_commit_sha":null,"homepage":"http://blackecho.github.io","language":"Python","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/gabrieleangeletti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-08-17T07:08:07.000Z","updated_at":"2024-10-23T20:19:07.000Z","dependencies_parsed_at":"2022-09-09T06:51:18.195Z","dependency_job_id":null,"html_url":"https://github.com/gabrieleangeletti/Deep-Learning-TensorFlow","commit_stats":null,"previous_names":["blackecho/deep-learning-tensorflow"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrieleangeletti%2FDeep-Learning-TensorFlow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrieleangeletti%2FDeep-Learning-TensorFlow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrieleangeletti%2FDeep-Learning-TensorFlow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gabrieleangeletti%2FDeep-Learning-TensorFlow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gabrieleangeletti","download_url":"https://codeload.github.com/gabrieleangeletti/Deep-Learning-TensorFlow/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226686730,"owners_count":17666928,"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":["deep-learning","tensorflow"],"created_at":"2024-08-07T23:02:57.039Z","updated_at":"2024-11-27T04:31:23.501Z","avatar_url":"https://github.com/gabrieleangeletti.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# Deep Learning algorithms with TensorFlow\n\nThis repository is a collection of various Deep Learning algorithms implemented using the\n[TensorFlow](http://www.tensorflow.org) library. This package is intended as a command line utility you can use to quickly train and\nevaluate popular Deep Learning models and maybe use them as benchmark/baseline in comparison to your custom models/datasets.\nIf you want to use the package from ipython or maybe integrate it in your code, I published a pip package named `yadlt`: Yet Another Deep Learning Tool.\n\n### Requirements:\n\n* tensorflow \u003e= 1.0\n\n### List of available models:\n\n* Convolutional Network\n* Restricted Boltzmann Machine\n* Deep Belief Network\n* Deep Autoencoder as stack of RBMs\n* Denoising Autoencoder\n* Stacked Denoising Autoencoder\n* Deep Autoencoder as stack of Denoising Autoencoders\n* MultiLayer Perceptron\n* Logistic Regression\n\n### Installation\n\n#### Through pip:\n\n    pip install yadlt\n\nYou can learn the basic usage of the models by looking at the ``command_line/`` directory. Or you can take a look at the [documentation](http://deep-learning-tensorflow.readthedocs.io/en/latest/).\n\n**Note**: the documentation is still a work in progress for the pip package, but the package usage is very simple. The classes have a sklearn-like interface, so basically you just have to create the object\n(e.g. `sdae = StackedDenoisingAutoencoder()`) and call the fit/predict methods, and the pretrain() method if the model supports it\n(e.g. `sdae.pretrain(X_train, y_train)`, `sdae.fit(X_train, y_train)` and `predictions = sdae.predict(X_test)`)\n\n#### Through github:\n\n* cd in a directory where you want to store the project, e.g. ``/home/me``\n* clone the repository: ``git clone https://github.com/blackecho/Deep-Learning-TensorFlow.git``\n* ``cd Deep-Learning-TensorFlow``\n* now you can configure the software and run the models (see the [documentation](http://deep-learning-tensorflow.readthedocs.io/en/latest/))!\n\n### Documentation:\n\nYou can find the documentation for this project at this [link](http://deep-learning-tensorflow.readthedocs.io/en/latest/).\n\n### Models TODO list\n\n* Recurrent Networks (LSTMs)\n* Variational Autoencoders\n* Deep Q Reinforcement Learning\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgabrieleangeletti%2FDeep-Learning-TensorFlow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgabrieleangeletti%2FDeep-Learning-TensorFlow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgabrieleangeletti%2FDeep-Learning-TensorFlow/lists"}