Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library

Machine Learning from scratch!

Update: Code implementations have been moved to python module. Notebook will only show results and model comparison

To refresh my knowledge, I will attempt to implement some basic machine learning algorithms from scratch using only python and limited numpy/pandas function. My model implementations will be compared to existing models from popular ML library (sklearn)

• Linear Regression with weight decay (L2 regularization)
• Logistic Regression with weight decay
• Random Forest with Permutation Feature Importances
  • Random Forest Regressor
  • Random Forest Classifier
• K Nearest Neighbors: supervised and unsupervised
• Neural network for classification
  • Stochastic Gradient Descent
  • Multiple hidden layers
  • Variety of activation functions + gradients (Sigmoid, Softmax, ReLU …) customized for each hidden layer
  • L2 regularization
  • Dropout
  • Dynamic learning rate optimizer (momentum, RMSProp and Adam)
  • TODO: batchnorm

The following notebooks uses Pytorch libraries so they are not implemented from scratch. However, I try not to use any high level Pytorch function

• Pytorch Neural Network with:
  • Custom Data Loader
  • Data Augmentation on 1 channel image: torchvision vs fastai
  • Shallow NN with batchnorm and dropout
  • Learning rate finder
• Auto Encoding
• Collaborative Filtering
• Char RNN in Vietnamese (Fast.ai)