Deep Learning Programming Assignment (MSE, Classifiers(Softmax, SVM), Backpropagation, Neural Network, MNIST CNN, ResNet)
In this assignment, you are asked to write a Python code that estimates the linear model with noise. The goal is to write a Python code to find x that minimizes MSE.
In this assignment, you are asked to write a Python code for three classifiers:
- k-Nearest Neighbor (kNN)
- SVM-loss classifier
- softmax classifier
In this assignment, you are asked to write a Python code for implementing a 2- layer neural network (a neural network with 1 hidden layer). Our goal is to implement the XOR operation.
There are classes defined for each layer:
- nn linear layer
- nn activation layer
- nn softmax layer
- nn cross entropy layer All of them have two common methods: forward and backprop.
In this assignment, there are two tasks.
- You are asked to write a Python code, writing methods for classes implementing convolutional and maxpool layer for neural networks.
- You are asked to label random images from MNIST dataset.
In this assignment, you are asked to write a Python code to implement a neural network classifier for MNIST dataset. Specifically, complete three methods, forward, backprop and update weights for class nn mnist classifier. You also need to complete all the layers in nn layers.py file.
In this assignment, you are asked to write a Python code to implement a classifier for CIFAR-10 dataset. Specifically, you need to implement a variant of ResNet whose blocks can model identity mapping. The implementation details are provided in the following sections.