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Solution to CS231n Assignments 2019
This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" (Spring 2020).
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 assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
Berkeley CS182/282A Designing, Visualizing and Understanding Deep Neural Networks
My own solutions for Stanford CS231n (2017) assignments
Notes and assignment solution to http://cs231n.stanford.edu
【更新完毕】斯坦福大学计算机视觉经典课程CS231n自学材料,总结了一些遇到的问题和知识点
Solutions for CS231n course from Stanford University: Convolutional Neural Networks for Visual Recognition
Assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition
My solutions to CS231N (Convolutional Neural Networks for Visual Recognition, Spring 2017)
assignment solution for Stanford CS231n 2018 spring
These are my solutions to the programming assignments of the class CS231n: Convolutional Neural Networks for Visual Recognition
Implemented Vanilla RNN and LSTM networks, combined these with pretrained VGG-16 on ImageNet to build image captioning models on Microsoft COCO dataset. Explored use of image gradients for generating new images and techniques used are Saliency Maps, Fooling Images and Class Visualization. Implemented image Style Transfer technique from 'Image Style Transfer Using Convolutional Neural Networks'. Implemented and trained GAN, LS-GAN and DC-GAN on MNIST dataset to produce images that resemble samples from MNIST, DC-GAN gave best resembling images.
Assignments for Stanford CS231n 2019 Spring
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.
My Solutions for the SPRING 2018 Assignments of CS231n: Convolutional Neural Networks for Visual Recognition
My solutions for assignments of EECS 498-007 / 598-005 class: Deep Learning for Computer Vision
Stanford CS231n Course: Convolutional Neural Networks for Visual Recogntion. All 3 assignments' solutions.
Stanford University's Famous computer Vision course 'CS231n' Assignments and codes.
My solutions to the assignments of Stanford course CS231n-2019
My solution to stanford cs231n: CNN for visual recognition
Solutions of CS231n Assignments Spring 2019
Solutions to the assignments of the 2018 Spring version of Stanford CS231n: Convolutional Neural Networks for Visual Recognition
My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition