Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library
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NN_pytorch.ipynb
README.md
autoencoder.ipynb
collab_filtering.ipynb
knn.ipynb
linear_regression.ipynb
logistic_regression.ipynb
neural_net_optimizers.ipynb
random_forest_classifier.ipynb
random_forest_regressor.ipynb
rnn-vietnamese.ipynb
scratch_neural_net.ipynb

README.md

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)

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