วีดีโอ

Hi Andrew, great video thanks. It appears to be that Anomaly detection here is treated as a binary problem: Either an anomaly is seen or not seen and if we apply this in network data, it quickly becomes a challenge with possible several false positives. I was wondering suppose that we look at the probability distribution based on the 2d plots we have seen here, and that given a new sample, based on the graph we can visually see that the new data point is anomalous, are there tools we could use to evaluate the performance without necessarily running the data through an ML algorithm?

thanks andrew ng

Thats a lot better than MIT lecture no doubt its for intellectuals i aint no intellectual neither brainy enough 😢

insightful

how to know the number of layers we need to have ?

this guy is amazing, the explanation is perfectly paced and doesn't assume anything besides very basic linear algebra and calculus skills

59:04

by far one of the most underrated TH-cam Channel, love your contents, keep going

Here NumPy implementation and in-depth explanation of key concepts for 1-D case m.th-cam.com/video/zFQUV4rMd2Q/w-d-xo.html

This is implementation of Linear regression from scratch in NumPy only. In-depth explanation of key concepts like Cost Function and Gradient Descent th-cam.com/video/wxCQxZKo4hU/w-d-xo.html

Thanks Andrew

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hi

Great, thanks

Brillian explanation, clear and easy to understand.

thanks to you Andrew 😉

As usual - Andrew NG brilliantly and clearly explains everything.

Fantastic explanation by professor Andrew NG ❤

It is a bit confusing when it comes to the design matrix

I have watched and studied the full course: an excellent introduction for understanding more advanced topics on this fascinating topic. Roger Hoefel - Wireless Researcher Thank you very much Andrew Ng!!

Thanks for the video. Very helpful. Could you also upload videos for the deep learning specification which is available on Coursera?

Aah. Thank a lot Andrew.