Data are becoming the new raw material of business
The Economist


Ranking Popular Deep Learning Libraries for Data Science

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At The Data Incubator, we pride ourselves on having the most up to date data science curriculum available. Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Here are the results.

The Rankings

Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average = score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average. See below for methods.

Results and Discussion

The ranking is based on equally weighing its three components: Github (stars and forks), Stack Overflow (tags and questions), and Google Results (total and quarterly growth rate). These were obtained using available APIs. Coming up with a comprehensive list of deep learning toolkits was tricky – in the end, we scraped five different lists that we thought were representative (see methods below for details). Computing standardized scores for each metric allows us to see which packages stand out in each category. The full ranking is here, while the raw data is here.
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The Many Facets of Artificial Intelligence

artificial-intelligence-2228610_960_720When you think of artificial intelligence (AI), do you envision C-3PO or matrix multiplication? HAL 9000 or pruning decision trees? This is an example of ambiguous language, and for a field which has gained so much traction in recent years, it’s particularly important that we think about and define what we mean by artificial intelligence – especially when communicating between managers, salespeople, and the technical side of things. These days, AI is often used as a synonym for deep learning, perhaps because both ideas entered popular tech-consciousness at the same time. In this article I’ll go over the big picture definition of AI and how it differs from machine learning and deep learning. Continue reading

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Machine Learning and Modeling the Stock Market: Alumni Spotlight on Michael Skarlinski

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Michael was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Schireson Associates.

 

Tell us about your background. How did it set you up to be a great data scientist? 

My PhD work was in computational materials science, where I worked with reactive molecular dynamics simulations. The field is totally simulation based, and typically requires high performance computing resources. Running these simulations helped build my chops for working with parallel systems and command line tools. The software required familiarity with some powerful languages and APIs like C and CUDA. Learning those definitely helped my understanding of Python once I converted to using it.

Toward the third year of my PhD I got really interested in machine learning. I started using scikit-learn to predict different aspects of simulations I worked on. These projects became a large part of my thesis and contributed to choosing The Data Incubator as a next step in my career.

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