reason? (I intuitively agree with you, just curious)

Here is one reason, but it's up for debate:

Deep learning courses rush through logistic regression and usually just mention SVMs. Arguably it's important for understanding deep learning to take the time to really, deeply understand how these linear models work, both theoretically and practically, both on synthetic data and on high dimensional real life data.

More generally, there are a lot of machine learning concepts that deep learning courses don't have enough time to introduce properly, so they just mention them, and you might get a mistaken impression about their relative importance.

Another related thing: right now machine learning competitions are dominated by gradient boosting. Deep learning, not really. This says nothing about **starting** with deep learning or not, but a good argument against **stopping** at deep learning.

In the last two days I alone wrote a prototype that can take a whiteboard photo, and automatically turn it into a mindmap-like zoomable chart. Pieces of the chart then can be rearranged and altered individually:

https://prezi.com/igaywhvnam2y/whiteboard-prezi-2015-12-04-152935/

This was part of a company hackathon, and I had some infrastructure to help me regarding the visualization, but with the shape recognition/extraction, it was just me and the nasty python bindings for OpenCV.

For some reason your link is not loading for me, but here is another CDC link: Percent Distributions of TBI-related Emergency Department Visits by Age Group and Injury Mechanism . Falls are big causes of TBI for the under four crowd and the over 65 crowd. The 5-14 and 45-64 groups also fall a lot... but only about half as much. **But**, also look at this: Percent Distributions of TBI-related Deaths by Age Group and Injury Mechanism.

Oh my god, look at 0-4-year old assaults, both ED visits and deaths. (Assault is the leading TBI-related cause of death for 0-4-year olds.) Some of those falling 4 year olds were assaulted.

Einstein repeatedly hit success in his lifetime

Actually he did not. He got lucky early in his career, and pretty much coasted on that into irrelevance. His intuition allowed him to solve problems related to relativity, the photoelectric effect, Brownian motion, and a few other significant contributions within the span of a decade, early in his career. And then he went off the deep end following his intuition down a number of dead-ending rabbit holes for the rest of his life. He died in Princeton in 1955 having made no further significant contributions to physics after is 1916 invention of general relativity. Within the physics community (I am a trained physicist), Einstein's story is retold more often as a cautionary tale than a model to emulate.

There are worse fates than not being able to top your own discovery of general relativity.

That's not a top-level comment, so it's excluded by my script from this version. I won't manually edit the output, sorry. There's another version where non-top-level comments are kept, too. Your quote is in there:

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Top quote contributors by karma score collected in 2014:

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*For*Bandana - 116 NancyLebovitz
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Top quote contributors by total (2009-2014) karma score collected:

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- 847 lukeprog
- 846 Eugine_Nier
- 841 GabrielDuquette
- 827 Eliezer_Yudkowsky
- 818 Stabilizer
- 775 Rain
- 750 MichaelGR
- 734 NancyLebovitz
- 628 Konkvistador
- 590 anonym
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- 434 RobinZ
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- 328 DSimon
- 296 Oscar_Cunningham
- 296 billswift
- 293 Pablo_Stafforini
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- 284 Nominull
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- 271 katydee
- 263 RolfAndreassen
- 262 Thomas
- 237 Kutta
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- Wilson 18
- Darwin 18
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- Dawkins 15
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- Sowell 14
- Silver 14
- Pinker 14
- Meier 14
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- Locke 12
- Egan 12
- Clark 12
- SMBC 11
- Plato 11
- Orwell 11
- Neumann 11
- Marx 11
- Holmes 11
- Hofstadter 11
- Hoffer 11
- Descartes 11
- Buffett 11
- Aurelius 11
- Turing 10
- Screwtape 10
- Peirce 10
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- Vinci 7
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- Lannister 7
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- Dijkstra 7
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- Carlyle 7
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Top original authors by karma collected:

- 894 Graham
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- 372 Dennett
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- 306 Aaronson
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- 249 Egan
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- 160 Hofstadter
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- 141 Wong
- 140 Confucius
- 136 Descartes
- 133 Brandon
- 130 Orwell
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- 127 Hayden
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- 123 Minsky
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- 123 Bakker
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- 117 Churchill
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Here is something I'd like to see: You give the machine the formally specified ruleset of a game (go, chess, etc), wait while the reinforcement learning does its job, and out comes a world-class computer player.