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Implementation of various ML and DL algorithms in Python
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Implementation of various Machine Learning and Deep Learning algorithms in Python.

Note: This is a work in progress repository. Feel free to raise issues if you face problems in running any notebook or have suggestion for improvement or you have any algorithm request.

  • Day 0: Linear Regression from scratch and using Scikit Learn
  • Day 1: Logistic Regression using Scikit Learn
  • Day 2: Logistic Regression from scratch Useful Link
  • Day 3: Implement Neural Network from scratch
  • Day 4: Implement Neural Network using Scikit Learn
  • Day 5: Submit solution to MNIST challenge on Kaggle with ~=.96 score
  • Day 6: Work on Fashion MNIST dataset using Tensorflow and Keras
  • Day 7: Learnt about Regularization, Mini Batch Gradient Descent, Learning Rate Optimizers (Momentum, RMS Prop, ADAM)
  • Day 8: Improve MNIST challenge score to 0.9735 using above techniques
  • Day 9: Learnt about Batch Normalization and Softmax Regression
  • Day 10: Learnt more about Tensorflow
  • Day 11: Learnt about Tensorboard

Day 12: Day 13: Day 14: Day 15: Day 16:

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