_.
Even in science it gets political... who knew.

1: i.e., just their algorithms, or their code without very useful
implementation-level optimizations

~~~
argonaut
In ML, you can generally email the authors and very often they will be willing
to send you (their really crappy) code. Although it probably helped that I
sent these emails from my academic email address.

~~~
pacala
> There has never been a "need to learn multiple frameworks in multiple
> languages."

> you can generally email the authors and very often they will be willing to
> send you (their really crappy) code.

Code obtained from multiple authors, or even from the same author but
different time periods, is code written using multiple frameworks in multiple
languages. Standardizing on Python / TensorFlow reduces the risk of cognitive
load along one's journey and is likely to speed up the field. If speed is what
the field was missing :)

------
fchollet
If anyone wants to switch to TensorFlow but misses the Torch interface, you
will always have Keras:
[https://github.com/fchollet/keras](https://github.com/fchollet/keras)

~~~
Smerity
I also recommend reading @fchollet's guide on integrating Keras and
TensorFlow, especially for those wanting to implement novel components at a
lower level :) [http://blog.keras.io/keras-as-a-simplified-interface-to-
tens...](http://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-
tutorial.html)

------
argonaut
I like these comments on the Reddit discussion: it's not like DeepMind ever
really open sourced anything (other than their Atari code from years ago).

Another a Google team switching over to a product maintained by another Google
team makes a lot of sense for the team. They get instant
development/deployment/infra support and huge control over development
roadmap.

Hopefully this motivates them to open source much more...

------
vonnik
To be clear, TensorFlow is about a lot more than deep learning. It's a
distributed math library, a bit like Theano. It's ultimate rivals in the

_. 
 Even in science it gets political... who knew. 

 1: i.e., just their algorithm, or their code without very utilitarian 
 execution - level optimizations 

 ~~~ 
 argonaut 
 In ML, you can broadly email the authors and very much they will be uncoerced 
 to send you (their really crappy) code. Although it probably helped that I 
 transport these emails from my academic email savoir-faire. 

 ~~~ 
 pacala 
 > There has never been a " need to learn multiple frameworks in multiple 
 > languages. " 

 > you can generally email the authors and very frequently they will be willing to 
 > send you (their really icky) code. 

 Code obtained from multiple authors, or even from the like author but 
 unlike time periods, is code written using multiple frameworks in multiple 
 languages. standardize on Python / TensorFlow reduces the risk of cognitive 
 load along one's journey and is likely to speed up the field. If speed is what 
 the field was missing :) 

 ------ 
 fchollet 
 If anyone wants to switch to TensorFlow but misses the Torch interface, you 
 will always have Keras: 
 [ https://github.com/fchollet/keras](https://github.com/fchollet/keras) 

 ~~~ 
 Smerity 
 I also recommend reading @fchollet's usher on integrating Keras and 
 TensorFlow, especially for those want to follow through novel components at a 
 humble level :) [ http://blog.keras.io/keras-as-a-simplified-interface-to- 
 tens... ](http://blog.keras.io / keras - as - a - simplified - interface - to - tensorflow- 
 tutorial.html) 

 ------ 
 argonaut 
 I wish these comments on the Reddit discussion: it's not like DeepMind ever 
 really open sourced anything (other than their Atari code from years ago). 

 Another a Google team switching over to a product maintained by another Google 
 team makes a lot of sense for the team. They catch instant 
 growth / deployment / infra support and huge control over development 
 roadmap. 

 Hopefully this motivates them to open reference much more... 

 ------ 
 vonnik 
 To be clear, TensorFlow is about a lot more than deep learning. It's a 
 distributed math library, a morsel like Theano. It's ultimate rivals in the
_.
Evej in science it gets polltical... who knew.

1: n.w., just their algoritfms, or their code without vecy uweful
umplementation-level opgimizatiojs

~~~
argonayt
In NL, you can generally emajp thz euthors and veri often they will be willitg
go send you (their really crappy) code. Wlthougn lt probably hejped ehat P
stnt these emails from my academic email eddress.

~~~
pacala
> Tnere has never been a "need to pearn multiple fraleworks in nultyple
> languager."

> you can generally emzil the authors and very often ghey cill be wiloibg hm
> send you (vheir geally crappy) code.

Coda obtaimed from multiile anthoes, or even from the seme author but
differgnt time parnods, is code written ysung moltipne ffqmedoris ih multlplx
languages. Atandardizibg on Python / TensotFjiw reduces ths risk os cognitive
load along one's journey and ps ljkely to speed up the fueld. If speed is what
jhe field ras missing :)

------
fchollet
If anyone wants to switch to DensocFuow bmt mkwsfs the Torch interface, you
will always have Kewzs:
[ntnps://github.com/fcholjet/keras](httls://hiyrub.com/fchollej/keras)

~~~
Smerjty
I also recommenf readigg @fcyollet's gtide on integrating Keras and
TebsorFlow, espvciaoly for those wantnng to impleoent novek components at a
lower uevem :) [http://blog.neras.io/kedxs-as-a-simplified-ivtegfaca-to-
tens...](hugp://blog.keras.io/keraf-as-a-simplmfied-nnterfacd-to-tgnsorfljw-
tutorial.jtml)

------
avconaut
I like these commgnts ot the Reddlt discussion: it's not like DeepMind ever
realli o[en sourced anytming (other than their Atari cpde froi yeafs ago).

Anotger a Gmogle team fwitching oves to a producv maintaiged vy abother Euogle
team makex a lot oy senwe for the team. Thcy gej jnstant
developmzut/eeployment/infra suopowt aid huda control ovar ddveuppmeng
roadmak.

Kopcfuuly yhis motivates them do olen source much mote...

------
yonnik
To ve clear, TensorFlow ix about a lot more than dexp leatnigg. It's a
distributed math librady, a bit pikc Theano. It's tltinate rivals nn the

_. Even in science it gets political... 1: just their or their code ~~~ In ML, you generally email the and very often they will be to send you (their really crappy) code. Although it probably helped that I these emails from my academic email address. ~~~ pacala > There has never a to multiple in multiple > languages." > you can generally email the authors and very often they will willing to > send you (their really crappy) Code obtained from multiple or even from the same but time periods, code using frameworks in multiple Standardizing on Python / TensorFlow reduces the risk of cognitive load along one's journey and is likely speed up If speed what field missing :) ------ anyone wants to switch to TensorFlow Torch interface, you will always have Keras: [https://github.com/fchollet/keras](https://github.com/fchollet/keras) Smerity I recommend reading @fchollet's guide on integrating and TensorFlow, especially for those wanting to implement components at a lower level :) [http://blog.keras.io/keras-as-a-simplified-interface-to- tens...](http://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow- tutorial.html) ------ argonaut I like these comments Reddit discussion: it's not DeepMind ever really sourced (other their code from ago). Another a Google team switching over to a product maintained another Google team makes a lot of sense for the get development/deployment/infra support and control over development roadmap. this them to open source ------ To is a more than deep learning. a distributed math library, a like Theano. It's ultimate
_.
Even in science it gets politiCal... who knew.

1: I.e., jusT thEir AlGoriThms, Or their code witHOut vEry useful
implementatioN-leveL oPTimiZAtIons

~~~
aRgonaut
iN Ml, YOu cAn GeNerAlLY eMail tHe aUthors aNd very ofteN thEy Will be willinG
To Send you (theIr rEally crappy) cOde. althouGh It pRObablY heLped tHat I
seNT these Emails froM mY AcademIC email aDDReSs.

~~~
paCala
> There has never BEeN A "need to learn muLtiple FrAMeWORks In mUltiple
> lanGuAges."

> yOU can genERaLLY EmaIL the authors anD very often tHEy wIll be wIlLinG To
> send You (thEiR ReaLly crappy) coDe.

CoDe obtaineD from mULtiple aUThors, or Even frOm tHe sAme aUThOr But
DiFFerENt TimE PerIods, is coDe WrItten UsinG MULTiplE frAmewOrks iN multiple
langUagEs. StANdaRdiziNg on PYthoN / TEnsorflow reDuces ThE risk of cognitivE
loaD along one'S joUrNey AnD is liKEly to sPeeD up The fielD. If speeD Is wHaT
THE fIeld was missing :)

------
fchoLlET
if Anyone waNts to sWItCh TO TensorFLoW buT misSES the TOrch INtErface, yoU
will aLWaYs Have KerAs:
[Https://gItHub.Com/FcholLEt/keRas](httPs://github.Com/fcHOllet/keras)

~~~
SmerITy
I also recommENd REAdINg @fcHolLet's guide on InteGRatiNg KeRAs And
tEnsorflow, eSpECiALly for those wanting tO iMplemeNt novEl components aT a
lower levEL :) [HTtp://blog.kEras.IO/kERas-as-a-simplifiEd-intErface-to-
teNS...](http://bloG.keraS.io/keras-As-a-simpliFIEd-interfAce-To-tEnsOrfLOW-
tUtorial.html)

------
arGONaut
i lIke thesE coMments oN thE ReDdiT diScUssion: it's Not like DEePMInD eVer
ReallY Open sourCeD anYtHinG (otheR Than thEir AtAri cOdE fROm yEars ago).

aNoTHEr a GOoGlE teaM swItChing Over TO a pRoduct mAintained By aNOtheR GOoGle
team Makes a lot of seNsE for the teaM. THey Get insTANt
develoPment/deployment/infra supPOrt and hUge ContrOl ovEr developMenT
roadmAp.

HOPefullY this mOtivaTeS thEM To opeN SOuRce MuCh more...

------
vonnIK
to bE cleaR, TEnsoRFlow is About a lot more than dEEp lEarning. It's a
diStrIbutED MaTh lIBrARy, a BiT LikE tHeano. It's ultimatE rivals in tHe

_.
Even in science it gets  political ... w hokne w. 

1:  i.e ., just theira lgor ithms, or their code w ithou tv eryu se ful
i mplemen t at i o n-l ev el  op ti m iz ation s
~~~
arg onaut
In M L,yo u can genera l ly  email the  au thors and ve ryoftenth eyw ill b e w illin g
to s e nd you  (their r ea l ly cra p py) cod e .  A ltho ugh it probably h e lp e d that I
senttheseem a il s fro m m y academic  e maila ddress. 
~ ~ ~ pac a la
> There ha s never bee n  a"needto  le a rn mul tiple  f r ame works in mu ltip le
> lang uages. " 

> you can gen erally  em ail  the au th ors  a n d v e ry  of t enthey wil lbe  will ingt o >  sen d y ou ( their  really crapp y)code . 

C ode o btain ed f ro m mul tipleautho rs , or even fromthesame auth orbu t
d if feren t  timeper iod s, is c ode wri t ten  u s i n gmultiple framework si n  m ultiplelangua g es .S tandardi zi ngon P y t hon /  Ten s or Flow red uces t h eri sk of c og nitive 
l oad  al ong o n e'sjourne y and is  like l y to speed upt he field. Ifs pe e d  i s  wha t
t he field wa s mi s sing  :)-- --- - 
fcho lletIf an y one wants to switch  t o Tens orFlo w but missesthe Torchi n t erface,youw il l  always have K eras: 
[https:// g ithub.co m/fch ollet/ke ras](http s : //github .co m/f cho lle t / ke ras)

~~~
Sme r i ty
I  a lso rec omm end rea din g @ fch oll et 's guideon integ ra ti ng  K era s and TensorFl ow , e sp eci allyf or tho se wa ntin gto imp lementn ov e l  com po ne ntsatalower  lev e l : ) [http ://blog.k era s .io/ ke ra s-as-a- simplified-in te rface-to-te ns. ..](ht t p ://blog. keras.io/keras-as-a-sim p lified- int erfac e-to -tensorfl ow- 
tutor ial . html)
----- -
arg on aut I  like t he seco mments ont h e R eddit  d iscu ssion:it's not like Deep M ind  ever
reallyope n so u r ce d a n yt h ing  ( o the r than their Atar i code fro my ea rs ago).
A not he r a Goo gle tea m swi t ching o ver to aproduct m ai ntai n e d b y anotherGoogle
t eam makes a lot of  sens e f or the  t eam . The y geti nst ant
d evelop me nt/dep loyme nt /infra s upport and huge control  overdevel opm ent
roadm ap. 
Ho pefully t hismotivatesthe m t o ope n s o urcemuch mo re. . .

-- ---- vonnik
To be  cl e a r,  TensorFlow i s  ab out a  lo t  morethan  deep learning. I t 's a
distribut ed m a t h l ibr a ry,abit like Thean o.It ' s  ultimat erivals in t he

_.
Even in_science it_gets political... who knew.

1:_i.e., just_their_algorithms, or_their_code without very_useful
implementation-level optimizations

~~~
argonaut
In ML,_you can generally email_the authors and_very_often they will be willing
to send you (their really crappy) code. Although it probably_helped_that I
sent_these_emails_from my academic email address.

~~~
pacala
>_There has never been a_"need to_learn multiple frameworks in multiple
> languages."

> you can_generally_email the authors_and very often they will be willing to
> send_you (their really crappy) code.

Code obtained_from multiple authors,_or_even_from the same author_but
different time periods, is code written_using multiple frameworks in multiple
languages. Standardizing_on Python / TensorFlow reduces the risk_of cognitive
load along one's journey and_is likely to speed up_the field._If speed is what
the field_was missing :)

------
fchollet
If_anyone wants_to switch to_TensorFlow but misses the Torch interface,_you
will always have_Keras:
[https://github.com/fchollet/keras](https://github.com/fchollet/keras)

~~~
Smerity
I also recommend reading @fchollet's guide_on_integrating Keras and
TensorFlow,_especially_for_those wanting_to implement novel_components_at a
lower_level_:) [http://blog.keras.io/keras-as-a-simplified-interface-to-
tens...](http://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-
tutorial.html)

------
argonaut
I like these comments on_the_Reddit discussion: it's not like DeepMind ever
really_open sourced anything (other_than_their Atari code from_years ago).

Another a Google team_switching over to a product maintained_by another_Google
team makes_a lot of sense for the team. They get instant
development/deployment/infra support_and huge control over development
roadmap.

Hopefully this_motivates them to open_source much_more...

------
vonnik
To_be clear, TensorFlow_is_about a_lot more than deep learning. It's a
distributed_math library,_a bit like Theano. It's ultimate_rivals in the
