Brainome 6-Week Webinar Series

6 Week Series

Core Tech 101 Starts in

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Register for the entire series today

Join us for this 6 week webinar series beginning October 27th as the Brainome team introduces the first-ever measure-before-build-tool for machine learning.

This series will feature 3 core technology webinars in which Gerald Friedland, Brainome’s CTO, will talk about how Brainome’s technology works and how it is disrupting the machine learning world. In addition you can join the 6 applications & benefits webinars, where you will learn about the core benefits of Daimensions™ our first product, and how to implement it now in your business.

Join the revolution in machine learning!

At Brainome we believe that machine learning, like all technical disciplines, must start with measurements.

By introducing the only “measure-first” machine learning solution, Brainome delivers an innovative “must have” streamlined flow for analyzing your data and creating models.

Brainome’s new approach is a fundamentally different way to do machine learning. We use precise measurements for a faster, more effective data preparation that accelerates feature engineering and quickly identifies the most predictive data attributes. Our models are tailored to your data, making them compact, fast to train, and explainable.

With Brainome, measure your success upfront.

Join us for this 6 week webinar series beginning October 27th as the Brainome team introduces the first-ever measure-before-build-tool for machine learning.

This series will feature 3 core technology webinars where you will hear Gerald Friedland, professor in Computer Science at UC Berkeley talk about the foundation of Brainome’s technology and how it will disrupt the Machine Learning industry. In addition you can join the 6 applications & benefits webinars, where you will learn about the core benefits of Daimension(TM), our first product, and how to implement it now in your business.

Meet your hosts

Gerald Friedland

Dr. Gerald Friedland is CTO of Brainome, Inc and is also teaching as an adjunct professor at the Electrical Engineering and Computer Sciences department of UC Berkeley. Before that, he had been with Lawrence Livermore National Lab and the International Computer Science Institute in Berkeley. His work focuses on signal processing and machine learning. He has published more than 250 peer-reviewed articles in conferences, journals, and 3 books. Dr. Friedland received his doctorate (summa cum laude) in computer science from Freie Universitaet Berlin, Germany, in 2006.