{"id":19580805,"url":"https://github.com/mirzaim/cs231n","last_synced_at":"2025-04-27T08:32:31.350Z","repository":{"id":41201623,"uuid":"419700986","full_name":"mirzaim/cs231n","owner":"mirzaim","description":"Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition","archived":false,"fork":false,"pushed_at":"2021-11-27T05:27:36.000Z","size":34350,"stargazers_count":44,"open_issues_count":0,"forks_count":15,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-04T23:33:07.505Z","etag":null,"topics":["2021","computer-vision","convolutional-neural-networks","cs231","cs231n","cs231n-assignment","deep-learning","machine-learning","neural-network","notes","pytorch","solutions","spring-2021","stanford"],"latest_commit_sha":null,"homepage":"https://mirzaim.github.io/cs231n/","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/mirzaim.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}},"created_at":"2021-10-21T11:47:30.000Z","updated_at":"2025-03-02T05:17:03.000Z","dependencies_parsed_at":"2022-07-14T10:31:23.555Z","dependency_job_id":null,"html_url":"https://github.com/mirzaim/cs231n","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/mirzaim%2Fcs231n","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaim%2Fcs231n/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaim%2Fcs231n/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mirzaim%2Fcs231n/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mirzaim","download_url":"https://codeload.github.com/mirzaim/cs231n/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251112589,"owners_count":21538162,"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":["2021","computer-vision","convolutional-neural-networks","cs231","cs231n","cs231n-assignment","deep-learning","machine-learning","neural-network","notes","pytorch","solutions","spring-2021","stanford"],"created_at":"2024-11-11T07:29:55.486Z","updated_at":"2025-04-27T08:32:26.337Z","avatar_url":"https://github.com/mirzaim.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CS231n: Convolutional Neural Networks for Visual Recognition - Spring 2021\nI've been following Stanford course [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) in my internship program at [Rayanesh company](http://rayaneshco.ir/). Here I gathered my notes and solutions to assignments. The course lectures were recorded in [Spring 2017](http://cs231n.stanford.edu/2017/), but the assignments are from [Spring 2021](http://cs231n.stanford.edu/2021/).\n\n## CS231n Assignments Solutions\nSome concepts in assignments like transformers or Self-Supervised learning are not taught in the 2017 lectures. Self-Supervised learning question is solved, but transformers question is skipped. The Style Transfer question was omitted in the 2021 assignments, so I returned to the 2017 homeworks to solve that.\n### Assignment 1:\nYou could get starter code from [here](https://cs231n.github.io/assignments2021/assignment1/).\n- [Q1](assignments/assignment1/knn.ipynb): k-Nearest Neighbor classifier. (_Done_)\n- [Q2](assignments/assignment1/svm.ipynb): Training a Support Vector Machine. (_Done_)\n- [Q3](assignments/assignment1/softmax.ipynb): Implement a Softmax classifier. (_Done_)\n- [Q4](assignments/assignment1/two_layer_net.ipynb): Two-Layer Neural Network. (_Done_)\n- [Q5](assignments/assignment1/features.ipynb): Higher Level Representations: Image Features. (_Done_)\n\n### Assignment 2:\nYou could get starter code from [here](https://cs231n.github.io/assignments2021/assignment2/).\n- [Q1](assignments/assignment2/FullyConnectedNets.ipynb): Multi-Layer Fully Connected Neural Networks. (_Done_)\n- [Q2](assignments/assignment2/BatchNormalization.ipynb): Batch Normalization. (_Done_)\n- [Q3](assignments/assignment2/Dropout.ipynb): Dropout. (_Done_)\n- [Q4](assignments/assignment2/ConvolutionalNetworks.ipynb): Convolutional Neural Networks. (_Done_)\n- [Q5](assignments/assignment2/PyTorch.ipynb): PyTorch / TensorFlow on CIFAR-10. (_Done in PyTorch_)\n\n### Assignment 3:\nYou could get starter code from [here](https://cs231n.github.io/assignments2021/assignment3/).\n- [Q1](assignments/assignment3/RNN_Captioning.ipynb): Image Captioning with Vanilla RNNs. (_Done_)\n- [Q2](assignments/assignment3/Transformer_Captioning.ipynb): Image Captioning with Transformers.\n- [Q3](assignments/assignment3/Network_Visualization.ipynb): Network Visualization: Saliency Maps, Class Visualization, and Fooling Images. (_Done_)\n- [Q4](assignments/assignment3/Generative_Adversarial_Networks.ipynb): Generative Adversarial Networks. (_Done_)\n- [Q5](assignments/assignment3/Self_Supervised_Learning.ipynb): Self-Supervised Learning for Image Classification. (_Done_)\n- [Extra](assignments/assignment3/LSTM_Captioning.ipynb): Image Captioning with LSTMs. (_Done_)\n\n### Assignment 3 - 2017:\n- [Q4](assignments/assignment3_2017/StyleTransfer-PyTorch.ipynb): Style Transfer. (_Done in PyTorch_)\n\n## CS231n 2017 Notes\nI took notes from some lectures.\n\u003c!-- - [Lecture 6](notes/Lecture6.md): Training Neural Networks, Part I.\n- [Lecture 7](notes/Lecture7.md): Training Neural Networks, part II.\n- [Lecture 8](notes/Lecture8.md): Deep Learning Software.\n- [Lecture 9](notes/Lecture9.md): CNN Architectures.\n- [Lecture 10](notes/Lecture10.md): Recurrent Neural Networks.\n- [Lecture 11](notes/Lecture11.md): Detection and Segmentation.\n- [Lecture 12](notes/Lecture12.md): Visualizing and Understanding.\n- [Lecture 13](notes/Lecture13.md): Generative Models. --\u003e\n- [Lecture 6](https://expensive-party-7a4.notion.site/Lecture-6-bf021569fee34d688365dd8d69457a95): Training Neural Networks, Part I.\n- [Lecture 7](https://expensive-party-7a4.notion.site/Lecture-7-9254463abc544b4ca496271fd43fc304): Training Neural Networks, part II.\n- [Lecture 8](https://expensive-party-7a4.notion.site/Lecture-8-5892d1482a4840fd9d32345ad9e9aa59): Deep Learning Software.\n- [Lecture 9](https://expensive-party-7a4.notion.site/Lecture-9-a8ee87cd54a948fbaba3f35655893d85): CNN Architectures.\n- [Lecture 10](https://expensive-party-7a4.notion.site/Lecture-10-313995a502b840c6beea12374a893f68): Recurrent Neural Networks.\n- [Lecture 11](https://expensive-party-7a4.notion.site/Lecture-11-936760fbedd04cba8ca52db5d7f3bd35): Detection and Segmentation.\n- [Lecture 12](https://expensive-party-7a4.notion.site/Lecture-12-9c3b45e36971487db6898233e4c28d90): Visualizing and Understanding.\n- [Lecture 13](https://expensive-party-7a4.notion.site/Lecture-13-ac2835438f644eadb9b083fa20a12f68): Generative Models.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmirzaim%2Fcs231n","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmirzaim%2Fcs231n","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmirzaim%2Fcs231n/lists"}