Data Science Weekly Newsletter

"Super Mario Kart" played by genetically evolved Neural Networks
I tried letting MarI/O learn to play Super Mario Kart, and it actually did pretty well...

Deep nets generating stuff
The last few weeks have been a time of neural nets generating stuff. By deep nets we mean recurrent and convolutional neural networks, while the stuff is text, music, images and even video...

Deep Learning to Fight Crime
We look at how using Deep Learning, Spark, and H2O Machine Learning platform can be used to analyze and predict crime in San Francisco and Chicago...

The Future Of Algorithmic Personalization
There are five main reasons why personalization remains broken...

Designing Machine Learning Models: A Tale of Precision and Recall at Airbnb
In this post, I give a brief overview of the thought process that comes with building a Machine Learning model. Of course every model is different, but hopefully it will give readers an insight on how we use data in a Machine Learning application to help protect our users, and the different approaches we use to improve our models. For this blog post, suppose we want to build a model to predict if certain fictional characters are evil*...

Machine Learning in FX: Going after a large scale implementation to imitate a trader’s brain
Today I want to share with you some of my thoughts around machine learning and how I have fully reevaluated the way in which I approach the subject. We will talk about how this potentially improves the quest for a better ML approach in order to to reach a state similar to what a “profitable trader’s brain” would optimally do...

A Statistical View of Deep Learning: What is Deep?
Throughout this series, we have discussed deep networks by examining prototypical instances of these models, e.g., deep feed-forward networks, deep auto-encoders, deep generative models, but have not yet interrogated the key word we have been using. We have not posed the question what does 'deep' mean, and what makes a model deep...

Google Made a Chatbot that debates the Meaning of Life
It reads like a scene from a classic sci-fi flick. But it’s not. It really is a human talking to a machine—a machine built by Google...

How Much Did It Rain? Winner's Interview: 1st place, Devin Anzelmo
An early insight into the importance of splitting the data on the number of radar scans in each row helped Devin Anzelmo take first place in the How Much Did It Rain? competition. In this blog, he gets into details on his approach and shares key visualizations (with code!) from his analysis...

Deep down the rabbit hole: CVPR 2015 and beyond
CVPR is the premier Computer Vision conference, and it's fair to think of it as the Olympics of Computer Vision Research... I have been going to CVPR almost every year since 2004, so let's take a brief tour at what's new in the exciting world of computer vision research...

Elusive Data Scientists Driving High Salaries
Recent study tracks experience, salary, industry and location of Data scientists, finds they are earning base salaries over $200K...

Data Scientist - GoButler - New York, NY GoButler is one of the hottest and fastest growing startups in recent years! We pursue a simple yet incredibly awesome idea: text GoButler whatever you need and we’ll make it happen! - It’s as simple as that. We are everybody’s private butler. If you find the idea of such a service as compelling as we do, come join the team :) ...

8 Best Machine Learning Cheat Sheets
Today, we have come up with best machine learning cheat sheets that are not too much in number but are simply worth it...

Of course. There is now a Hacker News for deep learning
Machine learning startup Startup.ML today unveiled a new website called Deep Learning News. It’s a place to go to find information online about deep learning...

Optimizing your model with cross-validation
In this video, we'll focus on K-fold cross-validation, an incredibly popular (and powerful) machine learning technique for model evaluation...

Machine Learning in Python: Essential Techniques for Predictive Analysis Simplifies machine learning for a broader audience and wider application by focusing on two algorithm families...
"I'm somewhat new to the field, working on a new venture involving predictive analytics. Of the resources I'm using, I keep coming back to this book - the background, problem setup, explanation of tradeoffs, and code examples have been really good..."... For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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