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I'm going through the course right now, and the instructor is saying some strange things, clearly (to me) ignoring that what he's saying is only true in very sp

by j2kun 11y ago

I'm going through the course right now, and the instructor is saying some strange things, clearly (to me) ignoring that what he's saying is only true in very specific contexts. For example, in the video I just watched he said "the natural way to compute the distance between two vectors is using cross entropy." And then he goes on to describe some unnatural features of cross entropy. The truly "natural" way to compute distances between vectors is the Euclidean distance, or at least any measure that has the properties of a metric. I can understand this is a crash course and there isn't time to cover nuances, but I'd much rather the instructor say things like "one common/popular way to do X is..." rather than making blanket and misleading statements. Or else how can I trust his claims about deep learning?

tibbe 11y ago
Because he wrote one of the most successful ones?
- j2kun 11y ago
  My goal is to understand it for my own purposes, not to put on blinders and replicate his work.
- GigabyteCoin 11y ago
  "Just trust me, I'm smarter than you are." isn't the best stance for a teacher to take.
stared 11y ago
It's a course assuming some background in ML. For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation. More on it: - http://stats.stackexchange.com/questions/80967/qualitively-what-is-cross-entropy http://stats.stackexchange.com/questions/80967/qualitively-w...
- j2kun 11y ago
  I understand it, I just disagree with the presentation. If you're sweeping complexity under the rug, say so and provide a link to further reading. I don't think cross-entropy is that common that someone in the target audience for this course would quickly and easily see the nuance.
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master_yoda_1 11y ago
I think you forget to read: ================================ Prerequisites and Requirements This is an intermediate to advanced level course. Prior to taking this course, and in addition to the prerequisites and requirements outlined for the Machine Learning Engineer Nanodegree program, you should possess the following experience and skills: Minimum 2 years of programming experience (preferably in Python) Git and GitHub experience (assignment code is in a GitHub repo) Basic machine learning knowledge (especially supervised learning) Basic statistics knowledge (mean, variance, standard deviation, etc.) Linear algebra (vectors, matrices, etc.) Calculus (differentiation, integration, partial derivatives, etc.) See the Technology Requirements for using Udacity.
sytelus 11y ago
Euclidean distance is not a good measure in higher dimension: http://stats.stackexchange.com/questions/29627/euclidean-distance-is-usually-not-good-for-sparse-data http://stats.stackexchange.com/questions/29627/euclidean-dis...
deleted 11y ago
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by j2kun 11y ago

tibbe 11y ago

Because he wrote one of the most successful ones?

j2kun 11y ago
My goal is to understand it for my own purposes, not to put on blinders and replicate his work.
GigabyteCoin 11y ago
"Just trust me, I'm smarter than you are." isn't the best stance for a teacher to take.

stared 11y ago

It's a course assuming some background in ML. For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation. More on it: - http://stats.stackexchange.com/questions/80967/qualitively-what-is-cross-entropy http://stats.stackexchange.com/questions/80967/qualitively-w...

j2kun 11y ago
I understand it, I just disagree with the presentation. If you're sweeping complexity under the rug, say so and provide a link to further reading. I don't think cross-entropy is that common that someone in the target audience for this course would quickly and easily see the nuance.
- deleted 11y ago
  [deleted]

master_yoda_1 11y ago

I think you forget to read: ================================ Prerequisites and Requirements This is an intermediate to advanced level course. Prior to taking this course, and in addition to the prerequisites and requirements outlined for the Machine Learning Engineer Nanodegree program, you should possess the following experience and skills: Minimum 2 years of programming experience (preferably in Python) Git and GitHub experience (assignment code is in a GitHub repo) Basic machine learning knowledge (especially supervised learning) Basic statistics knowledge (mean, variance, standard deviation, etc.) Linear algebra (vectors, matrices, etc.) Calculus (differentiation, integration, partial derivatives, etc.) See the Technology Requirements for using Udacity.

sytelus 11y ago

Euclidean distance is not a good measure in higher dimension: http://stats.stackexchange.com/questions/29627/euclidean-distance-is-usually-not-good-for-sparse-data http://stats.stackexchange.com/questions/29627/euclidean-dis...

deleted 11y ago

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