{"id":17606389,"url":"https://github.com/atcold/torch-video-tutorials","last_synced_at":"2025-04-05T15:09:10.851Z","repository":{"id":73551403,"uuid":"67192860","full_name":"Atcold/torch-Video-Tutorials","owner":"Atcold","description":"Light your way in Deep Learning with Torch 🔦","archived":false,"fork":false,"pushed_at":"2016-10-28T18:23:00.000Z","size":16639,"stargazers_count":595,"open_issues_count":0,"forks_count":98,"subscribers_count":50,"default_branch":"master","last_synced_at":"2025-03-29T14:12:22.166Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Lua","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/Atcold.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":"2016-09-02T05:29:23.000Z","updated_at":"2025-03-21T14:39:33.000Z","dependencies_parsed_at":"2023-02-25T10:45:18.496Z","dependency_job_id":null,"html_url":"https://github.com/Atcold/torch-Video-Tutorials","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/Atcold%2Ftorch-Video-Tutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atcold%2Ftorch-Video-Tutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atcold%2Ftorch-Video-Tutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atcold%2Ftorch-Video-Tutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Atcold","download_url":"https://codeload.github.com/Atcold/torch-Video-Tutorials/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247353746,"owners_count":20925329,"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":"2024-10-22T15:44:23.582Z","updated_at":"2025-04-05T15:09:10.831Z","avatar_url":"https://github.com/Atcold.png","language":"Lua","readme":"# Torch Video Tutorials\n\n\u003e *Light your way in Deep Learning with* Torch :flashlight:\n\nThis aims to be a growing collections of introductory video tutorials on the [*Torch*](http://torch.ch) ecosystem.\n*Torch* is one of the fastest and most flexible framework existing for Machine and Deep Learning.\nAnd yes, flexibility was used to come with an intimidating learning curve... until now.\n\nEnjoy the view of these videos, transcripts and quizes (you can find in the [`res`](res) folder together with some notes about how I made these videos).\n\n\n## 1 - Get the basics straight\n\n### 1.0 - An overview on *Lua* ([slides](res/1.0/slides.pdf))\n\n[![Practical 1.0 - Lua](http://img.youtube.com/vi/QLYLOPeI92g/0.jpg)](https://youtu.be/QLYLOPeI92g?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 1.1 - An overview on *Torch*’s `Tensor`s ([slides](res/1.1/slides.pdf))\n\n[![Practical 1.1 - Torch](http://img.youtube.com/vi/o3aRgD1uzsc/0.jpg)](https://youtu.be/o3aRgD1uzsc?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 1.2 - An overview on *Torch*’s `image` package ([slides](res/1.2/slides.pdf))\n\n[![Practical 1.2 - image package](http://img.youtube.com/vi/dEjvydjcwOE/0.jpg)](https://youtu.be/dEjvydjcwOE?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n\n## 2 - Artificial Neural Networks\n\n### 2.0 - Neural Networks – feed forward (inference) ([slides](res/2.0/slides.pdf), [quiz](res/2.0/quiz.tex))\n\n[![Practical 2.0 – NN forward](http://img.youtube.com/vi/hxA0wxibv8g/0.jpg)](https://youtu.be/hxA0wxibv8g?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 2.1 - Neural Networks – back propagation (training) ([slides](res/2.1/slides.pdf), [quiz](res/2.1/quiz.tex))\n\n[![Practical 2.1 - NN backward](http://img.youtube.com/vi/VaQUx7m3oR4/0.jpg)](https://youtu.be/VaQUx7m3oR4?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 2.2 - Neural Networks – An overview on *Torch*’s `nn` package ([slides](res/2.2/slides.pdf), [script](res/2.2/script.lua))\n\n[![Practical 2.2 - nn package](http://img.youtube.com/vi/atZYdZ8hVCw/0.jpg)](https://youtu.be/atZYdZ8hVCw?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n\n## 3 - Convolutional Neural Networks\n\n### 3.0 - CNN – Basics ([slides](res/3.0/slides.pdf), [`lin`](res/3.0/lin.lua), [`3conv`](res/3.0/3conv.lua), [`3conv-pool`](res/3.0/3conv-pool.lua))\n\n[![Practical 3.0 - CNN basics](http://img.youtube.com/vi/kwCbmx3tFwY/0.jpg)](https://youtu.be/kwCbmx3tFwY?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 3.1 - CNN – Internals ([slides](res/3.1/slides.pdf), [script](res/3.1/script.lua), [`3conv-pool`](res/3.0/3conv-pool.lua))\n\n[![Practical 3.1 - CNN internals](http://img.youtube.com/vi/BCensUz_gQ8/0.jpg)](https://youtu.be/BCensUz_gQ8?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 3.2 - CNN – Architectures ([slides](res/3.2/slides.pdf), [`LeNet5`](res/3.2/LeNet5.lua), [`AlexNet`](res/3.2/AlexNet.lua), [`GoogLeNet`](res/3.2/GoogLeNet.lua))\n\n[![Practical 3.2 - CNN models](http://img.youtube.com/vi/LYYwUr0vCjg/0.jpg)](https://youtu.be/LYYwUr0vCjg?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 3.3 - CNN – Training ([slides](res/3.3/slides.pdf), [`train.lua`](res/3.3/train.lua))\n\n[![Practical 3.3 - CNN models](http://img.youtube.com/vi/kcOJEplX7i0/0.jpg)](https://youtu.be/kcOJEplX7i0?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 3.4 - CNN – Loss functions ([slides](res/3.4/slides.pdf))\n\n[![Practical 3.4 - CNN loss](http://img.youtube.com/vi/ejr6eaJKtcs/0.jpg)](https://youtu.be/ejr6eaJKtcs?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n\n## 4 - Recurrent Neural Networks\n\n### 4.0 - RNN – Vectors and sequences ([slides](res/4.0/slides.pdf))\n\n[![Practical 4.0 - RNN, vec and seq](http://img.youtube.com/vi/bUIAsEw7_9U/0.jpg)](https://youtu.be/bUIAsEw7_9U?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 4.1 - RNN – Forward and backward ([slides](res/4.1/slides.pdf))\n\n[![Practical 4.1 - RNN, fwd and back](http://img.youtube.com/vi/WwslsYQX77s/0.jpg)](https://youtu.be/WwslsYQX77s?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 4.2 - RNN – `nngraph` package ([slides](res/4.2/slides.pdf), [script](res/4.2/script.lua))\n\n[![Practical 4.2 - nngraph package](http://img.youtube.com/vi/FL_VTcp9jvw/0.jpg)](https://youtu.be/FL_VTcp9jvw?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\n### 4.3 - RNN – Training ([slides](res/4.3/slides.pdf))\n\n[![Practical 4.3 - RNN training](http://img.youtube.com/vi/lRN0wayLTeo/0.jpg)](https://youtu.be/lRN0wayLTeo?list=PLLHTzKZzVU9ebuL6DCclzI54MrPNFGqbW)\n\nLSTM and training with `rnn` package coming soon! :blush:\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatcold%2Ftorch-video-tutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fatcold%2Ftorch-video-tutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatcold%2Ftorch-video-tutorials/lists"}