****** ********
****** ******** ~** ************ **** ****** *** ********** ****, ***** *****, ******* Ubuntu *** **** ******'* "******" **** ******** ******** kit ******* *********.

********* **** *** ******* a ************ *** ****** of ********* ******* ** what ** ******* ********'* ****** **** ******** intro ******, ***** **** ********** on ****-***** ******** ** artificial ************/**** ********.

Pre-Trained *******
*** **** ******* **** pre-loaded **** ********* **** previous ************** ********, ***** Nvidia had *** * ****** of ****** ****** **** a ******** *******, ** a ******* ********* ******** to ** ********** ********. The ***** ******** ******* is ********* *************** ********* and *** **** ******** from **** ** *****, depending ** *** ****** of ****** ***** *** into ** *** *** analysis ********. *** ****** of *** ******* ** a ********** ***** **** that ******** * **** of ********** ********* *** each ****** *** ****** has **** ******* ** recognize/classify. ***** ***** *** be *********** ** ***** powered *******, **** ** those ********* *** *** processor, ******** ***** ******* to **** ********* *** same ******* ** ***** images ** ***** ******.
Image ************** ********
** **** ** *** workshop, ********* ***** **** static ****** ********** * single ***** ** ********* fruits (*******, ******, ***.) to * ****** **** would ******* ** ******* known ******* *** **** light ** *** ************* to *** ************** ****. Next, ****** **** ******** objects **** *** **** a ****** **** ***** output *********** ** ***** requested ******* **** ***** in *** *****. ** each ** ***** ***** the ******** **** *** the ***** *** ~*-* seconds.

** *** ******* ********, the ******* ****** *** used ** ******* **** video. ******* ** ****** in ***** ** *** camera ******* ****** ** glass *******, ********* *** classifier *** **** ********** trained ** *********, *** supplied **** (* ****** script) ***** **** * red *** **** *** bottles. *** ****** ***** be ****** ** ****** the ***** ** ************ of *** ***, ****** a ***** ** *** objects ***** ** *** image, ** **** ***** programs ***** ** *********** a ***** ****** ****:

****** *** **** ***** was ********* ** ~****, analysis **** *** **** slower, **** * ******* 2 ****** ***, ****** analyzing ****** ******* *********, though **** *** ********* due ** *** ********** nature ** *** **** scripts.
Workshop *********
*** ******** ********* ** demystify ******** ** **** neural ********, *** ******** how ****** ******** *** *********** tools *** ** ******** to ****** ******* ********/********* in *** ******** ******** employing **** **********. *******, several ********* ********* **** the ***** *****-******* ********, spending **** **** ****** to **** *********** *** execute ******* **** ******* with ****** **** ****** network ************.
Developer *** ********
*** ***** ********** ** experimenting **** **** ********, Nvidia's developer **** *** ****** training ******* * *** to *** ******* ******* requiring ** ********* ********** in ****** ******** *** machine ******** ***********. *******, one ***** ***** **** significant ********** ** **** and ********** ******** ** attempt ** ****** * custom ********* *******/******** *** a ****-***** ***********. *****, understanding *** ************ ** the **************/******** *** ****** recognition ********* ***** ** helpful ** ********** **** neural *******-***** ******** *** customer ************.
Deep ******** ***
***** ******* ** ********** with ****** **** ******** ***** can *** **** *********** ********* **** ****** *** ~$***. If *** ** *** want ** ***** $*** just ** **********, ** ****** *** **** ****** pre-loaded *** ** **** ** on ****** (****: ******** AWS *******). ************, ***** with ** ****** ******** **** and * ** *** Ubuntu ******* (****** *******, or ******* *******) *** ******** DIGITS *** ****** [**** no ****** *********] (******** ************ in ****** ********* *******).
Nvidia Marketing **** ** ********
**** ****** *** ******** may **** **** **** attendees ********, ** *********** Nvidia's marketing ***** ** **** awareness ** ********. *** company ******* ~$**,*** ***** of ********* **** *** room *** * ** minute *******. ****** **** ********* a *********** ******* ** their ***** ** ***** companies **********, *** ****** ********* like********, ********* ****** ********** ** * variety ** ******** ** an ******* ** ********* the ********* ** ***** products.
***** ********* *********/******** *** ******** *** **** ********* *** security ******, *** *** not **** *** **** presence **** ****** *** ** ISC ****. *******, **** like ***-***** *** *********** are ***** **** ********* with *** ******* ****** of * ******, *** not *** ******** ********** inside, ** ******* ** be **** ** ****** ********* spend ** *** ******** industry **** ***** ********* sales ** ****** ******.
More ** **** ********?

Comments (5)
Undisclosed #1
Fascinating stuff. I am in no way a developer/coder, or even a moderately competent Linux user, but I do envy the opportunity to play around with this. Since Intel and Nvidia are heavily involved in deep learning does AMD/ATI have any foothold at this point? Based on what I've read on here it looks like Nvidia has a significant head start.
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Luca Michielli
I saw the conference at ISC West and it was min blowing.. I hope to see more article about AI in general and intelligent city from Nvidia.
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Andrew Hogendijk
Does anybody know if the code that they used to demonstrate the TX-1 is available? A quick look on the NVidia website didn't reveal anything. It would be fascinating to reproduce this.
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