Data Science Weekly Newsletter

Issue

129

May 12, 2016

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Editor's Picks

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Machine Learning for Emoji Trends
In Part 1 of this blog post series, we will take a deep dive into emoji usage on Instagram. By applying machine learning and natural language processing techniques, we’ll discover the hidden semantics of emoji...

The Good, Bad, & Ugly of TensorFlow
A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff)...

The next AI is no AI
AI technologies could evolve into a platform, an infrastructure similar to the Internet, that would allow people themselves to decide the way they utilize AI or contribute to its design and development. Such an AI grid, like the Internet of Things², powering various experiences and applications in different environments and industries, being open for tinkerers and specialists alike, would significantly change the way we could understand AI or interact with intelligent systems in general...

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Catenus Science Apprenticeship Program

The Catenus Science Apprenticeship Program identifies top data scientists who will raise the bar when hired at a startup. To help meet this goal, the program will train qualified candidates to have immediate, meaningful impact as data scientists in some of the top data startups in the world. This program will hone their skills in statistics, machine-learning, programming, and product development by presenting them with real-world challenges put forth by startups in Silicon Valley and the Bay Area.
We offer a fully-paid, 13-week apprenticeship
during which we reinforce technical and business skills. We do this via a mix of formal instruction and hands-on application of data science in some of the best startups in the world...

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Let’s Stop Writing Like It’s 1995
It’s not every day when a technology is created that fundamentally changes writing. Or even every year...

March Machine Learning Mania 2016, Winner's Interview:
1st Place, Miguel Alomar
The annual March Machine Learning Mania competition sponsored by SAP challenged Kagglers to predict the outcomes of every possible match-up in the 2016 men's NCAA basketball tournament. Nearly 600 teams competed, but only the first place forecasts were robust enough against upsets to top this year's bracket. In this blog post, Miguel Alomar describes how calculating the offensive and defensive efficiency played into his winning strategy...

Number plate recognition with Tensorflow
In order to get some hands-on experience with implementing neural networks I decided I’d design a system to solve a similar problem: Automated number plate recognition (automated license plate recognition if you’re in the US)...

Build an AI Composer - Machine Learning for Hackers #2
This video will get you up and running with your first AI composer in just 10 lines of Python. The app can compose british folk songs after training on an existing folk dataset...

Writing With The Machine
Responsive, inline “autocomplete” powered by an RNN trained on a corpus of old sci-fi stories...

Towards Conceptual Compression
We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable models for both the ImageNet and Omniglot datasets...

Using Machine Learning to Predict Out-Of-Sample Performance of Trading Algorithms
Earlier this year, we used DataRobot, a machine learning platform, to test a large number of preprocessing, imputation and classifier combinations to predict out-of-sample performance. In this blog post, I’ll take some time to first explain the results from a unique data set assembled from strategies run on Quantopian...

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Data Scientist - DataKind - New York
We’re looking for a self-directed data scientist to work with our full-time DataKind Labs team in delivering insights and quantitative expertise to our sector-wide initiatives. As a member of DataKind Labs, you will be working directly with our Head of Labs in this highly visible and high impact role, which will be equal parts challenging and rewarding. DataKind Labs projects are multi-party, year-long, full-time initiatives designed to bring innovative machine learning and data science solutions to social challenges...

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TPOT: A Python tool for automating data science
We’ve designed TPOT to be an end-to-end automated machine learning system, which can act as a drop-in replacement for any scikit-learn model that you’re currently using in your workflow...

What is Machine Learning — Explain like I am 5 years old
So as co-founder of a startup whose core offerings are AI technologies, I find myself explaining Machine Learning (or worse Deep Learning techniques that we use) to a lot of people (clients, potential investors, other enthusiasts) what exactly is Machine Learning and how can we use it. Here I will try to present the same points...

Bike Rental Demand Estimation with Microsoft R Server
In this blog post, we walk through how to use Microsoft R Server (MRS) to build a regression model to predict bike rental demand...

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Street-Fighting Mathematics:
The Art of Educated Guessing and Opportunistic Problem Solving Not a new book, though very well reviewed...
"This book is a treasure trove of intuitive, practical, and brilliant mathematical techniques. Every person with an interest in mathematics, science, or engineering will enjoy this highly stimulating and fun book.""... 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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