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


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.

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