From the course: Building Recommender Systems with Machine Learning and AI

Unlock this course with a free trial

Join today to access over 23,200 courses taught by industry experts.

Tuning restricted Boltzmann machines

Tuning restricted Boltzmann machines - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

Start my 1-month free trial Buy for my team

Tuning restricted Boltzmann machines

“

- So here's your challenge, see if we just have the wrong topology and parameters for our RBM. The right number of hit nodes, the right learning rates, the right bat size, and the right number of epochs needed to converge, all depend on the amount and the nature of our training data. So there is no single correct answers for RBMs in general. It's your job to tune the RBM for the data that we have. So give it a go, as we did before use SurpriseLib's grid search CV class, and pick a couple of hyper-parameters to work with. Keep trying different sets of values, using the results each time to converge on a better set of values to try next time. Until you have parameters that yield the best results. I'd encourage you to write this from scratch, if you're up for it, but you may notice that my solution is already in your course materials as the RBMtuning.py file. If you choose to cheat and just use my code, it's still a worthwhile exercise. As most of the work here is just the process of…

Contents