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Live Q&A with Matous Cejnek - Machine Learning with Python: Introduction to Clustering and Classification

Matous Cejnek - Watch Now - DSP Online Conference 2021 - Duration: 19:33

Live Q&A with Matous Cejnek - Machine Learning with Python: Introduction to Clustering and Classification
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Michael_Kirkhart
Score: 0 | 3 years ago | 1 reply

The sample image cannot be accessed - my browser reports a 403 - access forbidden error. It looks like it is not in the same location as the ipython notebooks.

Stephane.Boucher
Score: 0 | 3 years ago | no reply

Should be fixed now, thanks.

mpuchlik
Score: 0 | 3 years ago | no reply

Hello, very nice tutorial. Cheers!

Stephane.Boucher
Score: 1 | 3 years ago | no reply

The notebooks and sample image have been added to the Description of this talk (click on the 'Description' tab)

10:33:34	 From  Marcin Puchlik : How to apply ML in the DSP area? Any ideas?
10:36:13	 From  Dave Comer : I was think about how to apply ML to measure the vibration of a furnace to predict the failure, or health of the furnace. It seems as if the main challenge is to capture failure. Can you suggest how you might approach this?
10:37:21	 From  rakhel : great talk, I don't know nothing about ML, but I got most of the things that you went through in your talk. Thank you !
10:39:58	 From  Michael Kirkhart : Yesterday's talk on "Data Centric AI for Signal Processing Applications" discussed the use of an autoencoder for prediction of failures in air compressors.
10:41:49	 From  Dave Comer : Thanks Michael
10:41:54	 From  Leonard : here is an article where I synthesized the data and used random forest to categorize symbol
10:41:57	 From  Leonard : http://koliberengineering.blogspot.com/2020/02/categorical-estimation-of-qam-symbols_27.html
10:44:44	 From  Dave Comer : Definitely Michael. I would have to wait for a furnace to fail many time. That is my quandary...
10:45:39	 From  Dave Comer : Where can we get the Juypter file?
10:47:20	 From  Michael Kirkhart : @Dave Lomer: the autoencoder example from yesterday did not need data from furnace failures; it only needs data from a "normally operating" furnace.

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