Digests » 56

ai

Practical Deep Learning for Coders, v3

Welcome! If you’re new to all this deep learning stuff, then don’t worry—we’ll take you through it all step by step. We do however assume that you’ve been coding for at least a year, and also that (if you haven’t used Python before) you’ll be putting in the extra time to learn whatever Python you need as you go. (For learning Python, we have a list of python learning resources available.)

Machine Learning for the Mathematically Inept

This is a generalisation of Pythagoras’ theorem to apply to all triangles rather than just right angled ones. The cosine rule reduces to Pythagoras’ Theorem as well as providing the mathematical basis behind the usefulness of the dot product for establishing the extent to which two vectors are going in the same direction.

Using AWK and R to parse 25tb

Recently I was put in charge of setting up a workflow for dealing with a large amount of raw DNA sequencing (well technically a SNP chip) data for my lab. The goal was to be able to quickly get data for a given genetic location (called a SNP) for use for modeling etc.

Using ML for Campaign Optimization: Our Journey to Marketing Science at Automattic

My favorite description so far comes from the Marketing Science team at Facebook: Marketing Science transforms marketing efforts so that they are grounded in data and science.

Understanding cross-entropy

A couple of days ago a friend of mine, who started exploring deep learning, asked me “Hey man, can you explain this Cross-entropy thing to me?”. Now, that is a tough question, because this topic is never set with me right. My self-doubt kicked in, so in my mind, this question actually sounded more like “Can you explain Cross-entropy to yourself?”.