The Matrix Calculus You Need For Deep Learning

This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don’t worry if you get stuck at some point along the way—just go back and reread the previous section, and try writing down and working through some examples. And if you’re still stuck, we’re happy to answer your questions in the Theory category at

Handling Outliers with R

Recently, I attended a presentation where the following graph was shown illustrating the response to stimulation with Thalidomide among a cohort of HIV-1 patients. The biomarker used to measure the response in this case was TNF (tumor necrosis factor) and the response was measured at four time points: time of drug administration and 3, 11 and 23 weeks after administration. This graph appears in a published article, which I won´t cite directly because except for this one statistical problem, the research is outstanding.

Exception and Error Handling in Python

Error handling increases the robustness of your code, which guards against potential failures that would cause your program to exit in an uncontrolled fashion.

Machine Learning & Data Science at Github

What is the role of data science in product development at Github What does building data products at Github actually looks like

Correlating stock returns using Python

In this tutorial I’ll walk you through a simple methodology to correlate various stocks against each other. We’ll grab the prices of the selected stocks using the IEX api, drop them into a clean dataframe, run a correlation, and visualize our results.

AI at Google: our principles

At its heart, AI is computer programming that learns and adapts. It can´t solve every problem, but its potential to improve our lives is profound. At Google, we use AI to make products more useful – from email that´s spam-free and easier to compose, to a digital assistant you can speak to naturally, to photos that pop the fun stuff out for you to enjoy. Beyond our products, we´re using AI to help people tackle urgent problems. A pair of high school students are building AI-powered sensors to predict the risk of wildfires. Farmers are using it to monitor the health of their herds. Doctors are starting to use AI to help diagnose cancer and prevent blindness. These clear benefits are why Google invests heavily in AI research and development, and makes AI technologies widely available to others via our tools and open-source code. We recognize that such powerful technology raises equally powerful questions about its use. How AI is developed and used will have a significant impact on society for many years to come. As a leader in AI, we feel a deep responsibility to get this right. So today, we´re announcing seven principles to guide our work going forward. These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions. We acknowledge that this area is dynamic and evolving, and we will approach our work with humility, a commitment to internal and external engagement, and a willingness to adapt our approach as we learn over time.

Data Retrieval and Cleaning: Tracking Migratory Patterns

Advancing your skills is an important part of being a data scientist. When starting out, you mostly focus on learning a programming language, proper use of third party tools, displaying visualizations, and the theoretical understanding of statistical algorithms. The next step is to test your skills on more difficult data sets.

SQL Cheat Sheet

A good programmer or software developer should have a basic knowledge of SQL queries in order to be able retrieve data from a database. This cheat sheet can help you get started in your learning, or provide a useful resource for those working with SQL.

A Comparative Review of the Rattle GUI for R

Rattle is a popular free and open source Graphical User Interface (GUI) for the R software, one that focuses on beginners looking to point-and-click their way through data mining tasks. Such tasks are also referred to as machine learning or predictive analytics. Rattle´s name is an acronym for ‘R Analytical Tool To Learn Easily.’Rattle is available on Windows, Mac, and Linux systems. This post is one of a series of reviews which aim to help non-programmers choose the GUI that is best for them. Additionally, these reviews include a cursory description of the programming support that each GUI offers.

Elo and EloBeta models in snooker

Research about adequacy of Elo based models applied to snooker match results. Contains a novel approach (EloBeta) targeted for sport results with variable ‘best of N’ format.

Invariant Causal Prediction for Sequential Data

We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X 1 ,…,X d ) . Classical ordinary least squares regression includes all predictors that reduce the variance of Y . Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions, loosely speaking they lead to invariance across different ‘environments’ or ‘heterogeneity patterns’. More precisely, the conditional distribution of Y given its causal predictors remains invariant for all observations. Recent work exploits such a stability to infer causal relations from data with different but known environments. We show that even without having knowledge of the environments or heterogeneity pattern, inferring causal relations is possible for time-ordered (or any other type of sequentially ordered) data. In particular, this allows detecting instantaneous causal relations in multivariate linear time series which is usually not the case for Granger causality. Besides novel methodology, we provide statistical confidence bounds and asymptotic detection results for inferring causal predictors, and present an application to monetary policy in macroeconomics.