Learn what the exploration-exploitation tradeoff is, and how to use your model's uncertainty to explore new items in a wise manner.
Learn how to estimate model uncertainty in neural networks.
Interpreting deep learning models is hard. Learn how to use uncertainty estimates to get insights about what the model has learned.
Learn how to use Gumbel distribution to form a NN containing a discrete random component.
Summary of TCE conference - "Deep Learning: Theory & Practice".
Our implementation and usage of hyperparameter tuning at Taboola.
How to employ word2vec's embeddings and A* search algorithm to morph between words.
Learn how to train a neural network to use inputs from multiple different modalities using the GMU block.