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Photo Credit [The 2nd YouTube-8M Video Understanding Challenge](http://The 2nd YouTube-8M Video Understanding Challenge) has just finished. Google generously handed out $300 Google Cloud Platform(GCP) credits to the first 200 eligible people, and I was lucky enough to be one of them. I wouldn’t be able to participate in this challenge at a higher level otherwise. My local hardware can barely handle the size of the dataset and is not strong enough to handle the size of the model. The least I can do to return the favor is to write a short tutorial on how to set up deep-learning-ready VMs on GCP and about some tips that I’ve learned. ...

Photo Credit We’ve discussed what quantile regression is and how does it work in Part 1. In this Part 2 we’re going to explore how to train quantile regression models in deep learning models and gradient boosting trees. Source Code The source code to this post is provided in this repository: ceshine/quantile-regression-tensorflow. It is a fork of strongio/quantile-regression-tensorflow, with following modifcations: Use the example dataset from the scikit-learn example. The TensorFlow implementation is mostly the same as in strongio/quantile-regression-tensorflow. ...

Photo Credit I’m starting to think prediction interval[1] should be a required output of every real-world regression model. You need to know the uncertainty behind each point estimation. Otherwise the predictions are often not actionable. For example, consider historical sales of an item under a certain circumstance are (10000, 10, 50, 100). Standard least squares method gives you an estimate of 2540. If you restock based on that prediction, you’re likely going to significantly overstock 75% of the time. The prediction is almost useless. But if you estimate the quantiles of the data distribution, the estimated 5th, 50th, and 95th percentiles are 16, 75, 8515, which are much more informative than the 2540 single estimation. It is also the idea of quantile regression. ...

Photo Credit Introduction Jigsaw toxic comment classification challenge features a multi-label text classification problem with a highly imbalanced dataset. The test set used originally was revealed to be already public on the Internet, so a new dataset was released mid-competition, and the evaluation metric was changed from Log Loss to AUC. I tried a few ideas after building up my PyTorch pipeline but did not find any innovative approach that looks promising. Text normalization is the only strategy I had found to give solid improvements, but it is very time consuming. The final result (105th place/about top 3%) was quite fitting IMO given the time I spent on this competition(not a lot). ...

Source Introduction NLP(Natural-language processing) is hard, partly because human is hard to understand. We need good tools to help us analyze texts. Even if the texts are eventually fed into a black box model, doing exploratory analysis is very likely to help you get a better model. I’ve heard great things about a R package tidytext and recently decided to give it a try. The package authors also wrote a book about it and kindly released it online: Text Mining with R: A guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools. ...