In the presentation below, Seth Juarez of DevExpress discusses architecting predictive algorithms for machine learning.
Trying to show the data analysis package R is no more scary than Excel, John Mount of the Win-Vector blog shows a simple analysis both in Excel and in R.
In the talk below, Recursive Deep Learning for Modeling Compositional and Grounded Meaning, Richard Socher, Founder, MetaMind describes deep learning algorithms that learn representations for language that are useful for solving a variety of complex language tasks.
In this slide deck presentation below, Jake VanderPlas, discusses how you can use your coding skills to “hack statistics” – to replace some of the theory and jargon with intuitive computational approaches such as sampling, shuffling, cross-validation, and Bayesian methods.
Deep Learning is a hot topic in statistical learning and many data scientists are seeking a place to start. Here is a presentation from the July 23rd SF Machine Learning Meetup at the Workday Inc. San Francisco office. The featured speaker is Ilya Sutskever.
The talk below, “Topological Data Analysis for the Working Data Scientist” was presented at the SF Data Mining meetup group. Speaker Anthony Bak begins with a short review of the Mapper algorithm and discuss how to think about problems in the topological framework.
In this short presentation, Dr. Andrew Ng (world renowned deep learning luminary, Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty) talks about what’s going on with deep learning and how it is rapidly changing the problem domains that can be addressed with machine learning. In particular, Ng announces a deep learning […]
Jeff Dean of Google presented this talk at BayLearn 2015. “In this talk, I’ll highlight some of the lessons we have learned in using our first-generation distributed training system and discuss some of the design choices in our second-generation system. I’ll then discuss ways in which we have applied this work to a variety of problems in Google’s products, usually in close collaboration with other teams.”
Liz Crawford, CTO of Birchbox, presented at Data Driven NYC in October 2015. She gave a behind-the-scenes look at Birchbox’s Data Science & Analytics practice.
In this special guest feature for our Data Science 101 channel, Smita Adhikary of Big Data Analytics Hires highlights how data scientists sometimes tend to get bogged down in the “how” of a problem rather than the “why” of it, and end up delivering highly predictive, yet essentially meaningless models for the business.