words about stuff
This post will be about a cool new feature engineering technique for encoding sets of vectors as a single vector - as described in the recent paper An efficient manifold density estimator for all recommendation systems. The paper focuses on EMDE’s applications to recommender systems but I’m more interested in the technique itself. I will provide motivation for the technique, a python implementation of it and finally …
Read on →In this post I explain why graph embedding is cool, why Pytorch BigGraph is a cool way to do it and show how to use PBG on two very simple examples - the “Hello World!” of graph embedding. All the code can be found here. With this you can quickly get started embedding your own graphs. Example: Graph of movies Before we get started, here’s a motivating example: visualisation of the Movies Dataset from Kaggle.
Read on →I recently came across several articles about failing data science projects (according to Gartner 85% big data projects are never fully productionised). The articles blame misaligned objectives, management resistance, unrealistic expectations, poor communication with stakeholders, poor data infrastructure. I think this is basically correct but too diplomatic. Here’s what I think: The typical data science project …
Read on →This is the second of a series of posts about things I wish someone had told me when I was first considering a career in data science. Part 1. For the purposes of this post I define a data analyst as someone who uses tools like Excel and SQL to interrogate data to produce reports, plots, recommendations but crucially doesn’t deliver code. If you work in online retail and create an algorithm recommending tiaras for …
Read on →This is the first of a series of posts about things I wish someone had told me when I was first considering a career in data science. Part 2 A popular meme places data science at the intersection of hacking, statistics and domain knowledge. It isn’t exactly untrue but it may give an aspiring data scientist the mistaken impression that those three areas are equally important. They’re not. I’m leaving domain knowledge …
Read on →TL;DR: I tested a bunch of neural network architectures plus SVM + NB on several text classification datasets. Results at the bottom of the post. Last year I wrote a post about using word embeddings like word2vec or GloVe for text classification. The embeddings in my benchmarks were used in a very crude way - by averaging word vectors for all words in a document and then plugging the result into a Random Forest. …
Read on →TL;DR: I prepared 5 puzzles about statistics that should be accessible to anyone without being trivial. Scroll down for the puzzles. Data Science and Statistics “Data science is statistics on a Mac” “Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” Then there is the famous Venn diagram with data …
Read on →This is a guest post by Javier Rodriguez Zaurin. My good friend Nadbor told me that he found on Reddit someone asking if data scientists end up doing boring tasks such as classifying shoes. As someone that has faced this problem in the past, I was committed to show that classifying shoes it is a challenging, entertaining task. Maybe the person who wrote that would find it more interesting if the objects to classify …
Read on →Trigger warnings: programming humor, algorithms and data structures, Java I’m interviewing data engineering contractors recently. All of the candidates are very senior people with 10+ years of experience. My go to question: Me: What data structure would you use (in your favorite programming language) to store a large number (let’s say 100k) of strings - so they can be looked up efficiently? And by …
Read on →This is the second post about my experiments with LSTMs. Here’s the first one. This is a great introduction by Karpathy. And this is an in depth explanation of the math behind. Python or Scala? Which should you use and when? Which should you learn first? Is type safety more important than flexibility? Is Python fast enough for performance-heavy applications? Is Scala’s machine learning ecosystem mature …
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