Stanford CS231n: Convolutional Neural Networks for Visual Recognition assignments and practices
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This project are assignment solutions and practices of Stanford class CS231n.
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The assignments are for 2017 years' version because video recordings are available on Youtube.
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For detailed information of the class, goto: CS231n: Convolutional Neural Networks for Visual Recognition
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Deep learning framework related assignments are finished with PyTorch.
- There are totally three collections of assignments, they are located in three directories.
- The assignments are updated with my solutions.
- For original quiz, follow instructions from each assignment readme documentation.
In this assignment you will practice putting together a simple image classification pipeline, based on the k-Nearest Neighbor or the SVM/Softmax classifier.
In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks.
In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. You will also explore methods for visualizing the features of a pretrained model on ImageNet, and also this model to implement Style Transfer. Finally, you will train a generative adversarial network to generate images that look like a training dataset!