{"id":13415678,"url":"https://github.com/alrojo/tensorflow-tutorial","last_synced_at":"2025-05-16T00:08:52.710Z","repository":{"id":94638740,"uuid":"64401630","full_name":"alrojo/tensorflow-tutorial","owner":"alrojo","description":"Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE","archived":false,"fork":false,"pushed_at":"2016-11-04T15:00:06.000Z","size":19024,"stargazers_count":1951,"open_issues_count":2,"forks_count":448,"subscribers_count":117,"default_branch":"master","last_synced_at":"2025-04-08T11:12:18.102Z","etag":null,"topics":[],"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/alrojo.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}},"created_at":"2016-07-28T14:19:56.000Z","updated_at":"2025-04-03T12:57:41.000Z","dependencies_parsed_at":"2023-03-30T08:36:05.585Z","dependency_job_id":null,"html_url":"https://github.com/alrojo/tensorflow-tutorial","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/alrojo%2Ftensorflow-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alrojo%2Ftensorflow-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alrojo%2Ftensorflow-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alrojo%2Ftensorflow-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alrojo","download_url":"https://codeload.github.com/alrojo/tensorflow-tutorial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254442854,"owners_count":22071878,"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":[],"created_at":"2024-07-30T21:00:51.278Z","updated_at":"2025-05-16T00:08:47.664Z","avatar_url":"https://github.com/alrojo.png","language":"Jupyter Notebook","readme":"# TensorFlow Tutorial - used by Nvidia\n\nLearn TensorFlow from scratch by examples and visualizations with interactive jupyter notebooks. Learn to compete in the [Kaggle leaf detection challenge](https://www.kaggle.com/c/leaf-classification)!\n\nAll exercises are designed to be run from a CPU on a laptop, but can be accelerated with GPU resources.\n\nLab 1-4 was used in the [Deep Learning using TensorFlow](http://www.eventbrite.com/e/deep-learning-using-tensorflow-tickets-27071720244#) in London by Nvidia and Persontyle\n\n## Credits\n\nLabs 1, 2, 3 and 5 have been translated from Theano/Lasagne with minor modifications from the following repositories: [Nvidia Summer Camp](https://github.com/DeepLearningDTU/nvidia_deep_learning_summercamp_2016) and [02456 deep learning](https://github.com/DeepLearningDTU/02456-deep-learning). Original authors: [skaae](https://github.com/skaae), [casperkaae](https://github.com/casperkaae) and [larsmaaloee](https://github.com/larsmaaloee).\n\nThanks to professor [Ole Winther](http://cogsys.imm.dtu.dk/staff/winther/) for supervision and sponsoring the labs.\n\n## Setup and Installation\n\nGuides for downloading and installing TensorFlow on Linux, OSX and Windows using Docker can be found [here](https://github.com/alrojo/tensorflow-tutorial/tree/master/download-and-setup).\n\n## Material\n\nThe material consists of 5 labs.\n\n### [Lab1 - FFN](https://github.com/alrojo/tensorflow-tutorial/tree/master/lab1_FFN)\n\nLogistic regression, feed forward neural network (FFN) on the (in)famous MNIST!\n\nOptional reading material from [Michael Nielsen](http://neuralnetworksanddeeplearning.com/) chapters 1-4 (Do 3-5 of the optional exercises).\n\n### [Lab2 - CNN](https://github.com/alrojo/tensorflow-tutorial/tree/master/lab2_CNN)\n\nConvolutional Neural Network (CNN) and Spatial Transformer on images.\n\nOptional reading material from [Michael Nielsen](http://neuralnetworksanddeeplearning.com/) chapter 6 (stop when reaching section called Other approaches to deep neural nets).\n\n### [Lab3 - RNN](https://github.com/alrojo/tensorflow-tutorial/tree/master/lab3_RNN)\n\nRecurrent Neural Network (RNN) on Translation using Encoder-Decoder model and Encoder-Decoder with attention.\n\nOptional reading material from [Alex Graves](https://www.cs.toronto.edu/~graves/preprint.pdf) chapters 3.1, 3.2 and 4,\n\n### [Lab4 - Kaggle](https://github.com/alrojo/tensorflow-tutorial/tree/master/lab4_Kaggle)\n\nCompete in the kaggle competition [Leaf Classification](https://www.kaggle.com/c/leaf-classification) using FFN, CNN and RNN.\n\n### [Lab5 - AE](https://github.com/alrojo/tensorflow-tutorial/tree/master/lab5_AE)\n\nUnsupervised learning with autoencoder (AE) reconstructing the MNIST from only two latent variables.\n\nOptional reading material from [deeplearningbook.org](http://www.deeplearningbook.org/contents/autoencoders.html) chapter 14.\n","funding_links":[],"categories":["Jupyter Notebook","📦 Legacy \u0026 Inactive Projects"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falrojo%2Ftensorflow-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falrojo%2Ftensorflow-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falrojo%2Ftensorflow-tutorial/lists"}