Machine Learning is one of the fastest growing areas of computer science, and Deep Learning (neural networks) is growing even faster, with lots of data and computing power at our fingertips. But what happens beyond the 101 samples of distinguishing between cats and dogs? We will look at how you attack real vision problems, what to consider, how to refine your models to get the most out of them and how to work Deep Learning projects more like Software Engineering projects than experiments.
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Deep Learning and Computer Vision in Practice
Tess is a developer/data scientist working at Microsoft. Over the past 20 years she has changed the way we do .net debugging, developed a large number of mobile apps. As of a couple of years ago she moved into the world of data science and machine learning working with a lot of the largest companies in Europe and beyond on really tough ML problems.