At: NAIC Location: New York, NYWeb: naic.orgPosition: Analyst I (Capital Markets)

We all know that open source is critical to data science, but we wanted to learn more about the process of creating these tools and the motivation driving the impressive people building them. Most of these developers contribute for free, because they believe in the good of the project. To get the inside scoop on open source, we talked to Olivier Grisel, a full-time open source developer and one of the core contributors behind the scikit-learn project, one of the most popular machine learning libraries in the world. We use scikit-learn a lot, and try to help support the project, but as Olivier explains, open source isn’t a perfect system. 

Last week, Sir Tim Berners-Lee announced Solid, a project designed to give users more control over their data. Solid is one of a number of recent attempts to rethink how the web works. As part of an effort to get my head around the goals of these different approaches and, more concretely, what they actually do, I made some notes on what I see as the most interesting approaches.

This is not at all related to Machine Learning.

After week 7, you will be able to communicate confidently which model features are the most important.

A new R package called crfsuite supported by BNOSAC landed safely on CRAN last week. The crfsuite package (https://github.com/bnosac/crfsuite) is an R package specific to Natural Language Processing and allows you to easily build and apply models for

Sponsored Post.By Seth Deland, Product Marketing Manager, Data Analytics, MathWorks

The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. Neural Approaches to Conversational AI

As humans, we can quickly adapt our actions in new situations, be it recognizing objects from a few examples, or learning new skills and applying them in a matter of just a few minutes. But when it comes to deep learning techniques, an understandably large amount of time and data is required. So the challenge is to help our deep models do the same thing we can - to learn and quickly adapt from only a few examples, and to continue to adapt as more data becomes available. This approach of learning to learn is called meta-learning, and being a hot topic, has seen a flurry of research papers using techniques like matching networks, memory-augmented networks, sequence generative models, fast reinforcement learning and many others.