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Machine Learning Crash Course: Part 4 - The Bias-Variance Dilemma

So what does this have to do with machine learning? Well, it turns out that machine learning algorithms are not that much different from our friend Doge: they often run the risk of over-extrapolating or over-interpolating from the data that they are trained on.

link: https://ml.berkeley.edu/blog/2017/07/13/tutorial-4
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Deep Learning in Computer Vision group in LinkedIn
https://www.linkedin.com/groups/10320678

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I heard about this part-time job position:

We are looking for a second or third year student who can assist the FAIMS project (in the Department of Ancient History) with Software Development. We are looking for a student familiar with XML, and Java (SQL a bonus). This position will be helping to create field research mobile applications for researchers around the world, and we will offer training in our specific software systems involved. No experience with Android is necessary, but training in automated testing via Robotium will be provided. This position will be part time for the remainder of the year, with possible promotion opportunities next year. The position is casual at HEW-3. Hours are flexible and we are happy to accommodate exam periods. Please send your CV to Dr Brian Ballsun-Stanton, brian@fedarch.org.
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