Below you'll find a list of all posts that have been categorized as “Machine Learning”
Author-topic models promise to give data scientists a tool to simultaneously gain insight about authorship and content in terms of latent topics. The model is closely related to Latent Dirichlet Allocation (LDA). Basically, each author can be associated with multiple documents, and each document can be attributed to multiple authors. The model learns topic representations for each author, so that …
Racing through 2016 with so much on the front burner and yet it is timely to pause for a quick update on the launch of my new machine learning company, RaRe Technologies. The Start of Something Exciting I’ve heard from a few people who were confused when they received a recent newsletter from “RaRe Technologies”, when they signed up for …
Our team was on site representing RaRe Technologies and Gensim at this year’s PyCon 2016 hosted in Portland, Oregon, from May 28th to June 5th. It was a packed, outright massive event of over 3000 attendees which included two days of focused tutorials, sponsor workshops and talks from some of the industry’s renowned experts. RaRe was a sponsor of the …
RaRe Technologies is deeply rooted in the open source community and we are always seeking out opportunities to dedicate our experience and time to the next generation of computer scientists. Often the first step is to connect ambitious students to the resources they need to truly make an impact with hands-on projects and mentorship. These up and coming students have …
As the demand for solid Machine Learning software development increases, RaRe has realized a need for an equally solid internal growth team and recently added two new hires into the mix. Chris Lakatos has joined the company as the Director of Marketing while Jeff Hoey has joined heading up Business Development. Each bring with them ample experience in technical fields …
Earlier this summer, our director Radim Řehůřek, led a talk about the state of Python in today’s world of Data Science. Covered in the talk is how businesses are using Python for commercial success, Python vs Java, and an interesting comparison of the popular latent semantic analysis (SVD) and word2vec algorithms running on with different platforms: Spark MLlib, gensim, scikit-learn …
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