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Machine Learning, Neural Nets, Deep Learning basics

Python 0.50% Jupyter Notebook 99.50%
ml backpropagation mlp neural-network python3 gmm mnist-classification lstm speech-denoising rnn-tensorflow

ml's Introduction

Machine Learning

  • speech-denoising: MLP and RNN based speech denoiser implementations using TensorFlow.

  • em.py: EM algorithm for a Mixture of Gaussians on one-dimensional data.

  • mnist-shallow.py: TensorFlow implementation of MLP (1024x5) using ReLU activation, He initialization and Adam optimization giving over 98% accuracy. Also analysing TSNE & PCA.

  • nw_compression.ipynb: Network compression of 'mnist-shallow' using low rank approximation of trained weights (using the top 20 singular values after SVD), thereby using only about 4% of the memory of the original network.

  • parity.py: MLP (4-4-1) with backprop from scratch to solve the parity problem for a 4-bit input.

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