June 21, 2022, 1 p.m. | Daniel Gutierrez

insideBIGDATA insidebigdata.com

Many new data scientists have voiced what they feel is the lack of a satisfying way to learn the concepts of back propagation/gradient computation in neural networks when taking undergrad level ML classes. So I thought I'd put together a number of useful learning resources to jump-start an understanding for this important process. The following list, curated from an informal Twitter poll, appears in no particular order.

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Data Scientist (m/f/x/d)

@ Symanto Research GmbH & Co. KG | Spain, Germany

Enterprise Data Quality, Senior Analyst

@ Toyota North America | Plano

Data Analyst & Audit Management Software (AMS) Coordinator

@ World Vision | Philippines - Home Working

Product Manager Power BI Platform Tech I&E Operational Insights

@ ING | HBP (Amsterdam - Haarlerbergpark)

Sr. Director, Software Engineering, Clinical Data Strategy

@ Moderna | USA-Washington-Seattle-1099 Stewart Street

Data Engineer (Data as a Service)

@ Xplor | Atlanta, GA, United States