Hands on Experience with Advanced Hyper-parameter Optimization Techniques, using Hyperopt

We'll go step by step, starting with what Hyper-parameter optimization is, we'll then implement a simple exhaustive search from scratch and do some exercises, after that we'll try Scikit-Learn's Grid Search and Random Search, we'll compare it with the more effective Hyper-Parameter Optimization Algorithm implemented in Hyperopt Library, TPE. We've included exercise for every part so that, you guys get a good understanding on what you are doing. We'll also go through on how to parallelize the evaluations using MongoDB making the optimization even more effective, and discuss the solution to general difficulties faced while making it work.

A Docker Image will be provided, so that participants won't have to waste time in setting up the environment.

The Workflow of the Workshop would be:

• We will start with a slide presentation so that participants get some insight on what they are going to do.
• After that we'll shift to Juypter Notebooks(pre-installed in the docker environment, so you can just focus on the implementation part), here you will implement the code, and see the algorithms of hyperparameter optimization in action. You can check out the notebooks and workflow more here on this github repo.
• After that we'll explain some code which uses hyperopt wrapper for Keras, to optimize hyper-parameter in Neural Networks.
• Also we'll show a demo of a problem that we were working on and solved using Hyperopt while at MateLabs.

After attending this workshop you will be able to apply Hyper-parameter optimization using better algorithms which decides the hyper-parameters based on previous information instead of just brute force techniques. In short much much efficient model training.

So, better attend it! ;)