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Field Report: Deep Learning Specialization on Coursera

This “Field Report” is a bit difference from all the other reports I’ve done for insideBIGDATA.com because it is more of a “virtual” report that chronicles my experiences going through the content of an exciting new learning resource designed to get budding AI technologists jump started into the field of Deep Learning. Renowned MOOC platform Coursera just launched a new Deep Learning Specialization series consisting of 5 courses.

RMSprop Optimization Algorithm for Gradient Descent with Neural Networks

The video lecture below on the RMSprop optimization method is from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. For all you AI practitioners out there, this technique should supplement your toolbox in a very useful way.

Sibyl Launches To Increase Utilization And Cut Impact of Costly Patient No-Shows

The founders of macro-eyes, a machine learning company that simplifies personalized patient care, announced the introduction of Sibyl, a predictive scheduling solution that cuts the financial and operational damage from patient No-Shows without relying on patient behavior change.

Interview: David Steinmetz, Machine Learning Engineer at Capital One

I recently caught up with Daniel Steinmetz, who is a Machine Learning Engineer with Capital One Bank to discuss how to get a job at Capital One, the types skills they are looking for, and what his typical day looks like.

The Importance of Vectorization Resurfaces

Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.

“Above the Trend Line” – Your Industry Rumor Central for 9/18/2017

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

‘Learning Database’ Speeds Queries from Hours to Seconds

University of Michigan researchers developed software called Verdict that enables existing databases to learn from each query a user submits, finding accurate answers without trawling through the same data again and again. Verdict allows databases to deliver answers more than 200 times faster while maintaining 99 percent accuracy. In a research environment, that could mean getting answers in seconds instead of hours or days.

“Above the Trend Line” – Your Industry Rumor Central for 9/11/2017

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Julia: A High-Level Language for Supercomputing and Big Data

Julia is a new language for technical computing that is meant to address the problem of language environments not designed to run efficiently on large compute clusters. It reads like Python or Octave, but performs as well as C. It has built-in primitives for multi-threading and distributed computing, allowing applications to scale to millions of cores. In addition to HPC, Julia is also gaining traction in the data science community.

“Above the Trend Line” – Your Industry Rumor Central for 9/4/2017

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.