****** ********
****** ******** ~** ************ **** ****** *** ********** ****, ***** *****, ******* 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 ** **** ********?
Can building a deep learning video analytics application be as easy as the typical VMS installation? Maybe not quite, but Nvidia is aiming to make it much easier with their developer kits and shared software libraries that can enable a decent linux user to have an analytics application up and running in a day.
Comments (5)
Undisclosed Integrator #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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