https://github.com/curt-park/cs231n_assignments
[Assignments] CS231N: Convolutional Neural Networks for Visual Recognition (2016 & 2017)
https://github.com/curt-park/cs231n_assignments
convolutional-neural-networks image-captioning recurrent-neural-networks visual-recognition
Last synced: 6 months ago
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[Assignments] CS231N: Convolutional Neural Networks for Visual Recognition (2016 & 2017)
- Host: GitHub
- URL: https://github.com/curt-park/cs231n_assignments
- Owner: Curt-Park
- Created: 2017-02-08T16:27:46.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-02-12T08:42:25.000Z (over 1 year ago)
- Last Synced: 2024-11-09T10:38:38.177Z (11 months ago)
- Topics: convolutional-neural-networks, image-captioning, recurrent-neural-networks, visual-recognition
- Language: Jupyter Notebook
- Homepage:
- Size: 37.9 MB
- Stars: 46
- Watchers: 5
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Repository for programming assignments of CS231n: Convolutional Neural Networks for Visual Recognition (2016 & 2017).
# 2017
### [Assignment1](http://cs231n.github.io/assignments2017/assignment1/)
- Q1: k-Nearest Neighbor classifier (20 points)
- Q2: Training a Support Vector Machine (25 points)
- Q3: Implement a Softmax classifier (20 points)
- Q4: Two-Layer Neural Network (25 points)
- Q5: Higher Level Representations: Image Features (10 points)
- Q6: Cool Bonus: Do something extra! (+10 points) - *Not done*
### [Assignment2](http://cs231n.github.io/assignments2017/assignment2/)
- Q1: Fully-connected Neural Network (25 points)
- Q2: Batch Normalization (25 points)
- Q3: Dropout (10 points)
- Q4: Convolutional Networks (30 points)
- Q5: PyTorch / TensorFlow on CIFAR-10 (10 points) - **Done both in Pytorch and Tensorflow**
- Q6: Do something extra! (up to +10 points) - **Done both in Pytorch and Tensorflow**
- Extra Credit: VGG-like networks which acheive 79.4% and 78.4% on CIFAR-10 test set in Pytorch and Tensorflow respectively
### [Assignment3](http://cs231n.github.io/assignments2017/assignment3/)
- Q1: Image Captioning with Vanilla RNNs (25 points)
- Q2: Image Captioning with LSTMs (30 points)
- Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points) - **Done both in Pytorch and Tensorflow**
- Q4: Style Transfer (15 points) - **Done both in Pytorch and Tensorflow**
- Q5: Generative Adversarial Networks (15 points) - **Done both in Pytorch and Tensorflow**
- Extra Credit: InfoGAN (TF), WGAN-GP (TF), WGAN-GP (Pytorch)
# 2016
### [Assignment1](http://cs231n.github.io/assignments2016/assignment1/)
- Q1: k-Nearest Neighbor classifier (20 points)
- Q2: Training a Support Vector Machine (25 points)
- Q3: Implement a Softmax classifier (20 points)
- Q4: Two-Layer Neural Network (25 points)
- Q5: Higher Level Representations: Image Features (10 points)
- Q6: Cool Bonus: Do something extra! (+10 points) - *Not done*
### [Assignment2](http://cs231n.github.io/assignments2016/assignment2/)
- Q1: Fully-connected Neural Network (30 points)
- Q2: Batch Normalization (30 points)
- Q3: Dropout (10 points)
- Q4: ConvNet on CIFAR-10 (30 points)
- Q5: Do something extra! (up to +10 points) - **Built a CNN network which acheives 73.5% on CIFAR-10 test set**
### [Assignment3](http://cs231n.github.io/assignments2016/assignment3/)
- Q1: Image Captioning with Vanilla RNNs (40 points)
- Q2: Image Captioning with LSTMs (35 points)
- Q3: Image Gradients: Saliency maps and Fooling Images (10 points)
- Q4: Image Generation: Classes, Inversion, DeepDream (15 points)
- Q5: Do something extra! (up to +10 points) - *Not done*
# Useful Links
- [Course Home](http://cs231n.stanford.edu/)
- [Syllabus](http://cs231n.stanford.edu/syllabus.html)
- [Lecture Note](http://cs231n.github.io/)