Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
Cs231n assignment solutions.
Completed Assignments for CS231n: Convolutional Neural Networks for Visual Recognition Spring 2017.
I have just finished the course online and this repo contains my solutions to the assignments! What a great place for diving into Deep Learning. Big thanks to all the fellas at CS231 Stanford!
Find course notes and assignments here and be sure to check out video lectrues for Winter 2016 and Spring 2017 !
Assignment 1:
- Q1 : k-Nearest Neighbor classifier. ( Done )
- Q2 : Training a Support Vector Machine. ( Done )
- Q3 : Implement a Softmax classifier. ( Done )
- Q4 : Two-Layer Neural Network. ( Done )
- Q5 : Higher Level Representations: Image Features. ( Done )
Assignment 2:
- Q1 : Fully-connected Neural Network. ( Done )
- Q2 : Batch Normalization. ( Done )
- Q3 : Dropout. ( Done )
- Q4 : Convolutional Networks. ( Done )
- Q5 : PyTorch / TensorFlow on CIFAR-10. ( Done in TensorFlow )
Assignment 3:
- Q1 : Image Captioning with Vanilla RNNs. ( Done )
- Q2 : Image Captioning with LSTMs. ( Done )
- Q3 : Network Visualization: Saliency maps, Class Visualization, and Fooling Images. ( Done in TensorFlow )
- Q4 : Style Transfer. ( Done in TensorFlow )
- Q5 : Generative Adversarial Networks. ( Done in TensorFlow )
CS231n: Convolutional Neural Networks for Visual Recognition - Spring 2021
Note and assignments for cs231n: convolutional neural networks for visual recognition.
I’ve been following Stanford course CS231n: Convolutional Neural Networks for Visual Recognition in my internship program at Rayanesh company . Here I gathered my notes and solutions to assignments. The course lectures were recorded in Spring 2017 , but the assignments are from Spring 2021 .
CS231n Assignments Solutions
Some concepts in assignments like transformers or Self-Supervised learning are not taught in the 2017 lectures. Self-Supervised learning question is solved, but transformers question is skipped. The Style Transfer question was omitted in the 2021 assignments, so I returned to the 2017 homeworks to solve that.
Assignment 1:
You could get starter code from here .
- Q1 : k-Nearest Neighbor classifier. ( Done )
- Q2 : Training a Support Vector Machine. ( Done )
- Q3 : Implement a Softmax classifier. ( Done )
- Q4 : Two-Layer Neural Network. ( Done )
- Q5 : Higher Level Representations: Image Features. ( Done )
Assignment 2:
- Q1 : Multi-Layer Fully Connected Neural Networks. ( Done )
- Q2 : Batch Normalization. ( Done )
- Q3 : Dropout. ( Done )
- Q4 : Convolutional Neural Networks. ( Done )
- Q5 : PyTorch / TensorFlow on CIFAR-10. ( Done in PyTorch )
Assignment 3:
- Q1 : Image Captioning with Vanilla RNNs. ( Done )
- Q2 : Image Captioning with Transformers.
- Q3 : Network Visualization: Saliency Maps, Class Visualization, and Fooling Images. ( Done )
- Q4 : Generative Adversarial Networks. ( Done )
- Q5 : Self-Supervised Learning for Image Classification. ( Done )
- Extra : Image Captioning with LSTMs. ( Done )
Assignment 3 - 2017:
- Q4 : Style Transfer. ( Done in PyTorch )
CS231n 2017 Notes
I took notes from some lectures.
- Lecture 6 : Training Neural Networks, Part I.
- Lecture 7 : Training Neural Networks, part II.
- Lecture 8 : Deep Learning Software.
- Lecture 9 : CNN Architectures.
- Lecture 10 : Recurrent Neural Networks.
- Lecture 11 : Detection and Segmentation.
- Lecture 12 : Visualizing and Understanding.
- Lecture 13 : Generative Models.
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Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
MahanFathi/CS231
Folders and files, repository files navigation, cs231n assignment solutions.
Completed Assignments for CS231n: Convolutional Neural Networks for Visual Recognition Spring 2017.
I have just finished the course online and this repo contains my solutions to the assignments! What a great place for diving into Deep Learning. Big thanks to all the fellas at CS231 Stanford!
Find course notes and assignments here and be sure to check out video lectrues for Winter 2016 and Spring 2017 !
Assignment 1:
- Q1 : k-Nearest Neighbor classifier. ( Done )
- Q2 : Training a Support Vector Machine. ( Done )
- Q3 : Implement a Softmax classifier. ( Done )
- Q4 : Two-Layer Neural Network. ( Done )
- Q5 : Higher Level Representations: Image Features. ( Done )
Assignment 2:
- Q1 : Fully-connected Neural Network. ( Done )
- Q2 : Batch Normalization. ( Done )
- Q3 : Dropout. ( Done )
- Q4 : Convolutional Networks. ( Done )
- Q5 : PyTorch / TensorFlow on CIFAR-10. ( Done in TensorFlow )
Assignment 3:
- Q1 : Image Captioning with Vanilla RNNs. ( Done )
- Q2 : Image Captioning with LSTMs. ( Done )
- Q3 : Network Visualization: Saliency maps, Class Visualization, and Fooling Images. ( Done in TensorFlow )
- Q4 : Style Transfer. ( Done in TensorFlow )
- Q5 : Generative Adversarial Networks. ( Done in TensorFlow )
- Jupyter Notebook 97.7%
- Python 2.3%
CS231n: Convolutional Neural Networks for Visual Recognition
Spring 2017, course description, instructors.
Teaching Assistants
Class Time and Location
Office hours, grading policy, course discussions, assignment details, course project details, prerequisites.
- Proficiency in Python, high-level familiarity in C/C++ All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who aren't as familiar with Python), but some of the deep learning libraries we may look at later in the class are written in C++. If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.
- College Calculus, Linear Algebra (e.g. MATH 19 or 41, MATH 51) You should be comfortable taking derivatives and understanding matrix vector operations and notation.
- Basic Probability and Statistics (e.g. CS 109 or other stats course) You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
- Equivalent knowledge of CS229 (Machine Learning) We will be formulating cost functions, taking derivatives and performing optimization with gradient descent.
IMAGES
VIDEO
COMMENTS
These are my solutions for the CS231n course assignments offered by Stanford University (Spring 2021). Solutions work for further years like 2022, 2023. Inline questions are explained in detail, the code is brief and commented (see examples below). From what I investigated, these should be the shortest code solutions (excluding open-ended ...
This repository contains my solutions to the assignments of the CS231n course offered by Stanford University (Spring 2018).. Find course notes and assignments here and be sure to check out the video lectures for Winter 2016 and Spring 2017!. Assignments using Tensorflow are completed, those using Pytorch will be implemented in the future.
CS231n Assignment Solutions. Completed Assignments for CS231n: Convolutional Neural Networks for Visual Recognition Spring 2017. I have just finished the course online and this repo contains my solutions to the assignments! What a great place for diving into Deep Learning. Big thanks to all the fellas at CS231 Stanford!
1. Open collect_submission.ipynb in Colab and execute the notebook cells. This notebook/script will: Generate a zip file of your code ( .py and .ipynb) called a1_code_submission.zip. Convert all notebooks into a single PDF file. If your submission for this step was successful, you should see the following display message:
To set up a virtual environment, run the following: cd assignment1. sudo pip install virtualenv # This may already be installed. virtualenv -p python3 .env # Create a virtual environment (python3) # Note: you can also use "virtualenv .env" to use your default python (usually python 2.7) source .env/bin/activate # Activate the virtual environment.
Once you have completed the assignment question (i.e. reached the end of the notebook), you can save your edited files back to your Drive and move on to the next question. For your convenience, we also provide you with a code cell (the very last one) that automatically saves the modified files for that question back to your Drive.
Contribute to vancuong1216/cs231n-1 development by creating an account on GitHub. ... CS231n: Assignment Solutions. Convolutional Neural Networks for Visual Recognition. Stanford - Spring 2021. About. Overview. These are my solutions for the CS231n course assignemnts offered by Stanford University (Spring 2021). Inline questions are explained ...
If you worked in a group, please put the names of your study group at the top of your assignment. When in doubt about collaboration details, please ask us on Piazza. Honor Code: There are a number of solutions to assignments from past offerings of CS231n that have been posted online. We are aware of this, and expect that all work submitted by ...
This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" course (Spring 2020).. Stanford's CS231n is one of the best ways to dive into Deep Learning in general, in particular, into Computer Vision.
CS231n Assignments Solutions. Some concepts in assignments like transformers or Self-Supervised learning are not taught in the 2017 lectures. Self-Supervised learning question is solved, but transformers question is skipped. The Style Transfer question was omitted in the 2021 assignments, so I returned to the 2017 homeworks to solve that ...
Assignment solutions for Stanford CS231n-Spring 2021. I couldn't find any solution for Spring 2021 assignments, So I decided to publish my answers. I also take some notes from lectures. Here's the link to my Repo . How much time did it take to watch lectures/ do readings and complete assignments (any estimate could be helpful). I'm considering ...
Finally using argmax I find the closest instance to each query instance. k-fold cross validation In this part, we split our training data and labels into 5 folds and test the k for 1, 3, 5, 8, 10 ...
Tutorial for using Google Colab to work on the homework assignments for CS231N: http://cs231n.stanford.edu/Speaker: Moo Jin Kim (Head TA, Spring 2022)
Spring 2024 Assignments. Assignment #1: Image Classification, kNN, SVM, Softmax, Fully Connected Neural Network. (To be released) Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization. (To be released) Assignment #3: Image Captioning with RNNs and Transformers, Network Visualization ...
A tutorial for working on the assignments for CS231n (https://cs231n.github.io/) in Google Colab.
There will be three assignments which will improve both your theoretical understanding and your practical skills. All assignments will contain programming parts and written questions. For practical reasons, in office hours, TAs have been asked to not look at students' code. Credit. Assignment 1 (10%): Image Classification, kNN, SVM, Softmax ...
In the notebook Generative_Adversarial_Networks.ipynb you will learn how to generate images that match a training dataset and use these models to improve classifier performance when training on a large amount of unlabeled data and a small amount of labeled data.When first opening the notebook, go to Runtime > Change runtime type and set Hardware accelerator to GPU.
This course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
CS231n Assignments Solutions Spring 2020. The last weeks I have been following the course of Stanford CS231n: Convolutional Neural Networks for Visual Recognition and this repository is a compilation of my solutions for the assignments proposed on the course. In each folder you will find a README.md file with the instructions for the ...
Each student must write down the solutions independently (without referring to written notes from the joint session) and hand in one assignment per student. If you worked in a group, please put the names of your study group on your assignment on top. Honor Code: There are a number of solutions to assignments from past offerings of CS231n that ...
CS231n Assignment Solutions. Completed Assignments for CS231n: Convolutional Neural Networks for Visual Recognition Spring 2017. I have just finished the course online and this repo contains my solutions to the assignments! What a great place for diving into Deep Learning. Big thanks to all the fellas at CS231 Stanford!
Illustration of LeCun et al. 1998 from CS231n 2017 Lecture 1 Convolutional neural network. Fei-Fei Li, Yunzhu Li, Ruohan Gao Lecture 1 - April 4, 2023 ... Assignment 1: Will be out Friday 4/7, due 4/21 by 11:59 PM - K-Nearest Neighbor ... Rule 1: Don't look at solutions or code that are not your own; everything you submit should be your own work
Assignment #1: 15% Assignment #2: 15% Assignment #3: 15% Midterm: 15% Final Project: 40%. Course Discussions Stanford students: Piazza Our Twitter account: @cs231n. ... There are a couple of courses concurrently offered with CS231n that are natural choices, such as CS231a (Computer Vision, by Prof. Silvio Savarese). Speak to the instructors if ...