README.md

🔬 CS231n & CV7062610: Assignment Solutions & Summary 📚

Computer Vision Concepts Summary 📷

Stanford & Ariel - Spring 2023 🌱

🌐 Overview

Welcome to the repository containing comprehensive summaries and assignment solutions for CS231n at Stanford University (2017) and CV7062610 at Ariel University (2023). This collection represents a synthesis of my personal insights gained from these courses, in combination with supplementary materials sourced from the internet. Please note that the summary is presented in a blend of English and Hebrew. It's important to emphasize that no instructor has reviewed or endorsed this summary. The information provided is founded on my own interpretations.

📝 Assignment Solutions

Assignment Instructions 🗣️:

• Each assignment folder contains its instructions and my solution.

How To Run 🏃‍♂️:

• It is advised to run in Colab, however, you can also run locally. To do so, first, set up your environment - either through conda or venv. It is advised to install PyTorch in advance with GPU acceleration. Then, follow the steps:

  1. Change every first code cell in .ipynb files to:

     %cd CV7062610/datasets/
     !bash get_datasets.sh
     %cd ../../

  2. Change the first code cell in section Fast Layers in ConvolutionalNetworks.ipynb to:

     %cd CV7062610
     !python setup.py build_ext --inplace
     %cd ..

Additionally, install the requirements specified under each assignment folder.

📚 Summary

📖 Key Resources

• Stanford Lecture videos (2017) - 📺 Lecture Videos
• Ariel Lecture Slides (2023) - Only Students can access that info...
  
  

🗺️🧭🔍 Summary Table of Contents

• 01. Linear Classifier & Cross Validation
• 02. Batch Normaliztion (More Details in 15)
• 03. Optimizations - SGD, Momentun, Nesterov, Adam & Dropout (More Details in 17)
• 04. Softmax & SVM
• 05. Analitic Gradient
• 06. Gradient Descent & Stochastic Gradient Descent
• 07. Image Features & ConvNets
• 08. Neural Networks
• 09. Activation Functions
• 10. Fully Connected Layer
• 11. 2-Layer Neural Networks & How To Compute Gradients
• 12. Convolution Layer
• 13. Pooling Layer
• 14. Weight Initialization - Xavier, Kaiming/MSRA
• 15. Batch Normaliztion (More detailed)
• 16. Transfer Learning
• 17. Optimizations (More details)
• 18. Enhancing CNN Robustness and Generalization - Data Augmentation, Fraction Pooling, Stochstic Depth, Cout & Mixup
• 19. CNN Architectures - AlexNet, VGGNet, GoogleNet & ResNet
• 20. GPT
• 21. DenseNet & Neural Architecture Search(NAS)
• 22. Practical Learning - Supervised, Unsupervised & Generative Modeling
• 23. FVBN - PixelRNN & PixelCNN
• 24. Autoencoders
• 25. Generative Adversial Networks (GAN's)
• 26. Pretext Tasks From Image Transformations
• 27. SimCLR, Moco & CPC
• 28. Generative Pre-Trained Transformers (GPT)
• 29. Detection & Segmentation
• 30. Computer Vision & Image Processing - Sensors
  
  

📄 Summary

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.