This is the Code for "How Do I Find the Best Hyperparameters?- The Math of Intelligence #7" By Siraj Raval on Youtube
Use Bayesian Optimization to find the optimal learning rate for a linear regression model. You can use any dataset you like (examples here) and you can use any "bayesian optimization library" you like as well. Bonus points are given if you are able to perform Bayesian optimization without using any libraries. Good luck!
This is the code for this video on Youtube by Siraj Raval as part of The Math of Intelligence series. I go through 3 strategies (grid search, random search, and bayesian optimization) to find the ideal hyperparameters. You can find a twitter sentiment dataset here or here.
- numpy
- matplotlib
- pandas
Install missing dependencies using pip.
You can run both the grid search and random search files using python name_of_file in terminal. I've got 2 examples of Bayesian Optimization in jupyter notebooks. The first one uses a scikit-learn wrapper to do it, but has great visualizations. The second one uses tensorflow to build the model, but implements bayesian optimization from scratch. You can run them both by typing jupyter notebook into terminal and the code will pop up in your browser.
Install jupyter here.
Credits for this code goes to David Li-Bland. I've merely created a wrapper to get people started.