Machine learning from a software engineer's perspective
Lot's of software engineers seem to avoid the field of machine learning because it seems hard. In this talk I want to give developers an intuition of what machine learning is using visual examples and without using mathematical formulas. I want to show that machine learning will make things possible that cannot be achieved using traditional procedural programming. I will identify high level components of a supervised machine learning algorithm: vectors, feature spaces, neural networks and labels.
An innovative Software Engineer who is always looking to apply novel techniques into practical and creative solutions. Tries to spot business opportunities that can be made possible by applying new and innovative techniques. Successful in applying machine learning, natural language processing and linear optimization algorithms in companies. Result driven, focuses on making proof of concepts of ideas instead of just focusing on theoretical solutions. Has a broad background in Software Development, Distributed Systems.