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In the 1940s, mathematical programming was synonymous with optimization. An optimization problem included an objective function that is to be maximized or minimized by choosing input values from an allowed set of values . Nowadays, optimization is a very familiar term in AI. Specifically, in Deep Learning problems
On Wednesday, March 11, 2020, I conducted the webinar titled “Monitoring & Orchestrating Your Microservices Landscape using Workflow Automation”. Not only was I overwhelmed by the number of attendees, but we also got a huge list of interesting questions before and especially during the webinar. Some of them were answered, but a lot of them were not. I want to answer all open questions in this series of seven blog posts. Today I am posting the final two in the series.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.