Computer Vision
I'm advised by Professor Jia Deng and Erich Liang in the Princeton Vision & Learning Lab.
Github: https://github.com/Michel-Liao
You can find past computer vision posts here!
Computer Vision Posts
Notes1
1. [All Coursera slides are from DeepLearning.AI]↩
Cool External Resources
Computer Vision Fundamentals
- Intro to Computer Vision by Prof. Hany Farid
- First Principles of Computer Vision by Prof. Shree Nayar
Neural Network Basics
Computer Graphics
PyTorch DataLoaders
- PyTorch DataLoaders and Datasets tutorial by Vihar Kurama
- Writing Custom Datasets, DataLoaders and Transforms by Sasank Chilamkurthy
Python Fundamentals
Projects2
- RNN and LSTM forward propagation from scratch
- Neural Style Transfer
- Used pre-trained VGG-19 to code NST
- Coded content and style cost function from scratch
- Face Recognition
- Used pre-trained FaceNet for face recognition and verification
- Coded triplet loss from scratch
- Image Segmentation
- Used TensorFlow to code a mini-U-Net for image segmentation with 85% accuracy (sparse categorical crossentropy)
- Car Detection
- Used TensorFlow to code non-max suppression and intersection over union to detect cars in images with pre-trained YOLO V2
- Alpaca Classifier
- Employed transfer learning on MobileNet V2 to classify alpacas with 95% accuracy
- Sign Language Multiclass Classification
- Used TensorFlow to build a RNN that classifies 10 signed numbers with 96% accuracy
- Cats and Dogs Classifier
- Used PyTorch to build a modified verison of AlexNet to classify images of cats and dogs
- Sign Language Multiclass Classification
- Used TensorFlow to build a CNN that classifies 6 signed numbers with 83% accuracy
- Facial Expression Classification
- Used TensorFlow to build a CNN that detects a smiling face with 93% accuracy
- Convolutional Neural Network from Scratch
- Coded padding, convolution, pooling, and backpropagation from scratch
- Optimization Methods
- Coded mini-batch gradient descent and stochastic gradient descent with momentum, RMSprop, Adam and fixed and scheduled learning rate decay from scratch
- 4-Layer Dense Neural Network
- Coded a 4-layer dense neural network from scratch to classify cat images with 82% accuracy
- 2-Layer Dense Neural Network for Classification
- Coded a 2-layer neural network from scratch to classify planar data with 90% accuracy
- Handwriting Perceptron Algorithm
- Coded MLP algorithm with 85% classification accuracy in Java
- Multiple Linear Regression for Housing Prices
- Coded multiple linear regression from scratch to predict housing prices
- Logistic Regression for Binary Classification
- Coded logistic regression from scratch to classify cat pictures with 70% accuracy
- Cross-correlation
- Coded cross-correlation from scratch on any image with any filter
2. * indicates ongoing project. All projects done in Python unless otherwise stated.↩
Want my computer vision posts in your inbox? Subscribe to my newsletter!