These are my solutions to the programming assignments of the class CS231n: Convolutional Neural Networks for Visual Recognition

Solutions for CS231n course from Stanford University: Convolutional Neural Networks for Visual Recognition

Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.

My solution to stanford cs231n: CNN for visual recognition

CS231n: Convolutional Neural Networks for Visual Recognition Assignments Implementation

My solutions to CS231N (Convolutional Neural Networks for Visual Recognition, Spring 2017)

Stanford university cs231n 2017 Spring

This will contain all the projects which I practice during the fast.ai workshop(Nurture AI)

My own solutions for Stanford CS231n (2017) assignments

Image Classification pipeline for CIFAR-10 dataset based on K-NN, Svm, Softmax and 2-layer Neural Net Classifiers

My solutions to CS231N CNN assignments

My solutions to public Stanford University course on Convolutional Neural Networks from Spring 2017

Code I wrote for CS231N by Stanford

My journey thorugh cs231n

my solution

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.