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: