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https://github.com/mirzaim/cs231n
Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
https://github.com/mirzaim/cs231n
2021 computer-vision convolutional-neural-networks cs231 cs231n cs231n-assignment deep-learning machine-learning neural-network notes pytorch solutions spring-2021 stanford
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Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
- Host: GitHub
- URL: https://github.com/mirzaim/cs231n
- Owner: mirzaim
- License: mit
- Created: 2021-10-21T11:47:30.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2021-11-27T05:27:36.000Z (almost 3 years ago)
- Last Synced: 2023-06-28T19:52:19.595Z (over 1 year ago)
- Topics: 2021, computer-vision, convolutional-neural-networks, cs231, cs231n, cs231n-assignment, deep-learning, machine-learning, neural-network, notes, pytorch, solutions, spring-2021, stanford
- Language: Jupyter Notebook
- Homepage: https://mirzaim.github.io/cs231n/
- Size: 32.8 MB
- Stars: 42
- Watchers: 1
- Forks: 11
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# CS231n: Convolutional Neural Networks for Visual Recognition - Spring 2021
I'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/).
## CS231n Assignments Solutions
Some 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.
### Assignment 1:
You could get starter code from [here](https://cs231n.github.io/assignments2021/assignment1/).
- [Q1](assignments/assignment1/knn.ipynb): k-Nearest Neighbor classifier. (_Done_)
- [Q2](assignments/assignment1/svm.ipynb): Training a Support Vector Machine. (_Done_)
- [Q3](assignments/assignment1/softmax.ipynb): Implement a Softmax classifier. (_Done_)
- [Q4](assignments/assignment1/two_layer_net.ipynb): Two-Layer Neural Network. (_Done_)
- [Q5](assignments/assignment1/features.ipynb): Higher Level Representations: Image Features. (_Done_)
### Assignment 2:
You could get starter code from [here](https://cs231n.github.io/assignments2021/assignment2/).
- [Q1](assignments/assignment2/FullyConnectedNets.ipynb): Multi-Layer Fully Connected Neural Networks. (_Done_)
- [Q2](assignments/assignment2/BatchNormalization.ipynb): Batch Normalization. (_Done_)
- [Q3](assignments/assignment2/Dropout.ipynb): Dropout. (_Done_)
- [Q4](assignments/assignment2/ConvolutionalNetworks.ipynb): Convolutional Neural Networks. (_Done_)
- [Q5](assignments/assignment2/PyTorch.ipynb): PyTorch / TensorFlow on CIFAR-10. (_Done in PyTorch_)
### Assignment 3:
You could get starter code from [here](https://cs231n.github.io/assignments2021/assignment3/).
- [Q1](assignments/assignment3/RNN_Captioning.ipynb): Image Captioning with Vanilla RNNs. (_Done_)
- [Q2](assignments/assignment3/Transformer_Captioning.ipynb): Image Captioning with Transformers.
- [Q3](assignments/assignment3/Network_Visualization.ipynb): Network Visualization: Saliency Maps, Class Visualization, and Fooling Images. (_Done_)
- [Q4](assignments/assignment3/Generative_Adversarial_Networks.ipynb): Generative Adversarial Networks. (_Done_)
- [Q5](assignments/assignment3/Self_Supervised_Learning.ipynb): Self-Supervised Learning for Image Classification. (_Done_)
- [Extra](assignments/assignment3/LSTM_Captioning.ipynb): Image Captioning with LSTMs. (_Done_)
### Assignment 3 - 2017:
- [Q4](assignments/assignment3_2017/StyleTransfer-PyTorch.ipynb): Style Transfer. (_Done in PyTorch_)
## CS231n 2017 Notes
I took notes from some lectures.
- [Lecture 6](https://expensive-party-7a4.notion.site/Lecture-6-bf021569fee34d688365dd8d69457a95): Training Neural Networks, Part I.
- [Lecture 7](https://expensive-party-7a4.notion.site/Lecture-7-9254463abc544b4ca496271fd43fc304): Training Neural Networks, part II.
- [Lecture 8](https://expensive-party-7a4.notion.site/Lecture-8-5892d1482a4840fd9d32345ad9e9aa59): Deep Learning Software.
- [Lecture 9](https://expensive-party-7a4.notion.site/Lecture-9-a8ee87cd54a948fbaba3f35655893d85): CNN Architectures.
- [Lecture 10](https://expensive-party-7a4.notion.site/Lecture-10-313995a502b840c6beea12374a893f68): Recurrent Neural Networks.
- [Lecture 11](https://expensive-party-7a4.notion.site/Lecture-11-936760fbedd04cba8ca52db5d7f3bd35): Detection and Segmentation.
- [Lecture 12](https://expensive-party-7a4.notion.site/Lecture-12-9c3b45e36971487db6898233e4c28d90): Visualizing and Understanding.
- [Lecture 13](https://expensive-party-7a4.notion.site/Lecture-13-ac2835438f644eadb9b083fa20a12f68): Generative Models.