MNIST-CLASSIFICATION-TUTORIAL Overview Algorithms Machine learning: LR, SVM, XGBoost, MLP Deep learning: CNN, ResNet, VAE, Distilling Knowledge, Data-Free Learning Framework Sklearn Tensorflow Pytorch Progress Model Framework Main Params Test Accuracy Time Cost /s Comments LR sklearn solver='liblinear', multi_class='ovr' 0.9202 57.87 SVM sklearn kernel='rbf', decision_function_shape='ovr' 0.9446 556.91 XGBoost sklearn max_depth=5, n_jobs=10 0.9651 141.38 MLP sklearn hidden_layer_sizes=(128, 32) 0.9768 44.80 MLP tensorflow batch_size=512, learning_rate=1e-3, hidden_layers=[128,32] 0.9795 39.05 CNN tensorflow batch_size=256, learning_rate=1e-5, num_epoch=100 0.9785 1062.03 ResNet VAE Distilling Knowledge Data-Free Learning Reference THE MNIST DATABASE of handwritten digits ConvNetJS MNIST demo Feed-Forward Neural Net for MNIST
