Solution to CS231n Assignments 2019
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Updated
Dec 8, 2022 - Jupyter Notebook
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).
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.
Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.
Berkeley CS182/282A Designing, Visualizing and Understanding Deep Neural Networks
Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
【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
My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition
My own solutions for Stanford CS231n (2017) assignments
Notes and assignment solution to http://cs231n.stanford.edu
Solutions to Assignments of CS231n: Convolutional Neural Networks for Visual Recognition(http://cs231n.github.io/)
Solutions for CS231n course from Stanford University: Convolutional Neural Networks for Visual Recognition
Assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition
All Solutions to Stanford CS231n Spring 2023 Course Assignments.
My solutions to CS231N (Convolutional Neural Networks for Visual Recognition, Spring 2017)
These are my solutions to the programming assignments of the class CS231n: Convolutional Neural Networks for Visual Recognition
assignment solution for Stanford CS231n 2018 spring
CS231n: Deep Learning for Computer Vision, Stanford - Spring 2023
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 St…
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