Data Science Weekly - Issue 126
Issue #126 April 21 2016
Editor Picks
How to Prevent a Plague of Dumb Chatbots
The best (and least annoying) chatbots will be those that recognize their limitations, and occasionally turn to humans for help...
Building a (semi) Autonomous Drone with Python
They might not be delivering our mail (or our burritos) yet, but drones are now simple, small, and affordable enough that they can be considered a toy. In this post, I'll show you how you can use Python and node.js to build a drone that moves all by itself...
Where Will Your Country Stand in World War III?
In this chapter, we use a network graph technique called Social Network Analysis (SNA) to map weapons transfer between countries. By analyzing bilateral weapons trade, a network of multilateral ties can be distilled, providing insights into the complex arena of international politics...
A Message from this week's Sponsor:
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Data Science Articles & Videos
Deep Learning AlphaGo under a Magnifying Glass:
How does the AI program manage to play superior Go?
In this blog, an overview is given of the different AI components of AlphaGo as well as details about the teaching material, learning methods and skills of the program...
Pride and Prejudice and Z-scores
You might think literary criticism is no place for statistical analysis, but given digital versions of the text you can, for example, use sentiment analysis to infer the dramatic arc of an Oscar Wilde novel. Now you can apply similar techniques to the works of Jane Austen thanks to Julia Silge's R package janeaustenr (available on CRAN)...
Guest Post (Part I): Demystifying Deep Reinforcement Learning
Still, while deep models for supervised and unsupervised learning have seen widespread adoption in the community, deep reinforcement learning has remained a bit of a mystery. In this blog post I will be trying to demystify this technique and understand the rationale behind it. The intended audience is someone who already has background in machine learning and possibly in neural networks, but hasn’t had time to delve into reinforcement learning yet...
Machine Learning Meets Economics, Part 2
In this article I show that even when a computer can perform a task more economically than a human, careful analysis suggests that humans and computers working together can sometimes yield even better business outcomes than simply replacing one with the other....
Every shot Kobe Bryant ever took. All 30,699 of them
Kobe Bryant's 30,699th and final field goal came from 19 feet with 31 seconds left against the Utah Jazz. During his 20 years with the Lakers, he fired up more than 30,000 shots, including the regular season and playoffs. Take a tour of key shots over his 20-year career, or explore the makes and misses over his long career on your own...
Visualizing Distributions
Demonstration of various ways to render a distribution with d3js...
Thought Experiments in the Browser
In some cases, the best aid to decision-making is less about finding “the answer” in the data and more about developing a deeper understanding of the underlying problem. In this post we will focus another tool that is often overlooked: interactive simulations through the means of agent based modeling...
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
In this paper we study the problem of image representation learning without human annotation. Following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection...
Jobs
Senior Data Scientist - Warby Parker - New York, NY Warby Parker is looking for a passionate senior data scientist to join our Technology team, helping build the next great lifestyle brand. With dozens of physical retail locations, a thriving e-commerce site, and a global supply chain, the Data Science team tackles a variety of exciting problems and domains. Working with lines of business owners and analysts, we have a direct, meaningful impact on the company. However, we know that we can do even more, so we’re searching for someone special to help grow our small (but mighty!) team...
Training & Resources
Stat212b: Topics Course on Deep Learning
This topics course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, text and data. We will delve into selected topics of Deep Learning, discussing recent models from both supervised and unsupervised learning. Special emphasis will be on convolutional architectures, invariance learning, unsupervised learning and non-convex optimization...
The Rubin Causal model - an introduction (and more)
Great set of short econometrics lectures...
Machine Learning Recipes with Josh Gordon
Very intuitive video series by J. Gordon to discover Machine Learning w/ sklearn and tensorflow...
Books
Python Crash Course: A Hands-On, Project-Based Introduction to Programming Thorough introduction to programming with Python...
"I have read multiple beginner guides to Python. I am currently up to chapter 11 in Python Crash Course. So far this is far and away my favorite Python programming book..."
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian