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Here are my assignment solutions for cs231n (Spring 2017). The course website: http://cs231n.stanford.edu/
I want to thank Stanford for making the lecture note, video and assignment available to all the people worldwide! This is truely a great course if you want to learn Convolutional Neural Networks and have a little stat or cs background. I myself feel so fruitful taking this!
For GPU based homework, Stanford made two choices:
- PyTorch
- Tensorflow
All my current solutions are subject to Tensorflow. All assignment should be completed and passed the test set.
If there is any errors or typo (or better solution!!!) please inform me!
Assignment list:
- Assignment #1
- 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)
- Assignment #2
- Q1: Fully-connected Neural Network (30 points)
- Q2: Batch Normalization (30 points)
- Q3: Dropout (10 points)
- Q4: Convolutional Networks (30 points)
- Q4: PyTorch/TensorFlow on CIFAR-10 (30 points) [TensorFlow]
- Assignment #3
- 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) [TensorFlow]
- Q4: Style Transfer (15 points) [TensorFlow; This one is pertty awesome]
- Q5: Generative Adversarial Networks (15 points) [TensorFlow]
There are a couple more solution on Winter 2016 that you may find helpful.