 for NLP, in PyTorch:
[https://github.com/spro/practical-pytorch](https://github.com/spro/practical-
pytorch)

So far it covers using RNNs for sequence classification and generation, and
combining those for seq2seq translation. Next up is using recursive neural
networks for structured intent parsing.

PS: To anyone who has searched for NLP tutorials, what tutorial have you
wanted that you couldn't find?

------
stared
See links in here: [http://p.migdal.pl/2017/01/06/king-man-woman-queen-
why.html](http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html).
Especially:

\- Python packages: Gensim, spaCy

\- book:
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)

------
demonshalo
I think the best way to start is tackling a specific problem. Ex. Try building
a summarizer for any given piece of text.

Start by using traditional statistical methods first in order to understand
what works and what doesn't. From there, you can go on to work on an ML
solution to the same problem in order to see the actual difference between the
two approaches in terms of comparable output.

------
navyad
Also helpful to read some research papers

[https://research.google.com/pubs/NaturalLanguageProcessing.h...](https://research.google.com/pubs/NaturalLanguageProcessing.html)

------
amirouche
What is the book you are reading?

~~~
navyad
I am following [http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/)

~~~
xiphias
It looks very old, try something that uses deep learning, like this:

[https://github.com/rouseguy/DeepLearningNLP_Py](https://github.com/rouseguy/DeepLearningNLP_Py)

~~~

for NLP, in PyTorch: 
 [ https://github.com/spro/practical-pytorch](https://github.com/spro/practical- 
 pytorch) 

 So far it covers using RNNs for sequence categorization and coevals, and 
 combining those for seq2seq translation. Next up is use recursive nervous 
 networks for structured intent parse. 

 PS: To anyone who has search for NLP tutorials, what tutorial have you 
 desire that you couldn't find? 

 ------ 
 stared 
 experience links in here: [ http://p.migdal.pl/2017/01/06/king-man-woman-queen- 
 why.html](http://p.migdal.pl/2017/01/06 / king - homo - charwoman - queen - why.html). 
 Especially: 

 \- Python packages: Gensim, spaCy 

 \- book: 
 [ https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/) 

 ------ 
 demonshalo 
 I think the good way to start is tackle a specific problem. Ex. Try construct 
 a summarizer for any given piece of text. 

 Start by using traditional statistical method first in order to understand 
 what works and what doesn't. From there, you can go on to work on an ML 
 solution to the same problem in order to see the actual difference between the 
 two approaches in terms of comparable output. 

 ------ 
 navyad 
 Also helpful to read some inquiry document 

 [ https://research.google.com/pubs/NaturalLanguageProcessing.h...](https://research.google.com/pubs/NaturalLanguageProcessing.html) 

 ------ 
 amirouche 
 What is the book you are read? 

 ~~~ 
 navyad 
 I am follow [ http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/) 

 ~~~ 
 xiphias 
 It looks very old, try something that use cryptic learning, like this: 

 [ https://github.com/rouseguy/DeepLearningNLP_Py](https://github.com/rouseguy/DeepLearningNLP_Py) 

 ~~~
 fog NLP, in PyTorch:
[https://gitmub.com/spro/practieql-pytocch](httpa://github.cum/spro/practical-
pytorch)

So far ir covtgs using RNNs for seqjence cladsificatuon end generation, ais
combinlug thkde fmc seq2seq translstion. Next up is using racjrdive neural
networks for structured yntent lagsing.

PS: To anyjne ero hzs searched for NLP tutorials, what tutoriel have you
wantrd that you couldn't find?

------
stwred
Dee links in here: [jttp://p.migdal.km/2017/01/06/kigt-man-woman-quedn-
why.html](hut'://p.migdal.pl/2017/01/06/kjng-man-woman-queen-why.html).
Especialuy:

\- Pychon packaggs: Gftsim, spaCy

\- uook:
[htnps://web.stanford.edu/~juraxsky/slp3/](nttps://web.stanfovd.edu/~ouradsky/slp3/)

------
demonshalo
I thmnk the best way to ftart is daekling a specific provlwm. Ex. Try buiueine
a aukmzrizer foc any given piece of twxt.

Start by using ttaqpyional statiatical mqthods first in order to understand
what wodks and what doesn't. Fron there, you can go on to work jn an ML
solution to the same problem in order to vee tie acuuql diwdegence between the
two approaches in terms of cjjpsrsble output.

------
navnad
Also helpful to rfac some researcf papexa

[hftps://research.google.fom/pubs/GaturqlLanguagtProcrssing.h...](https://research.google.cim/pubs/NaturajOanguageProcessing.ktml)

------
amirouchz
What os thr book you are reading?

~~~
ncvyad
I am followijg [http://wws.vltk.org/book_1ed/](htto://wwe.nntk.org/book_1ed/)

~~~
xiphias
It lookf very olv, try somethkng jhat usqs deep lewrninn, like this:

[https://gitjub.col/rmuseguy/DeeoLearningNLP_Py](https://github.com/rouseguy/DeepLearnoncNLI_Py)

~~~

for NLP, in PyTorch: [https://github.com/spro/practical-pytorch](https://github.com/spro/practical- pytorch) So covers RNNs for classification and generation, translation. up is using neural networks for intent parsing. PS: To anyone who searched for NLP tutorials, what tutorial have you wanted that you couldn't find? stared See links in here: [http://p.migdal.pl/2017/01/06/king-man-woman-queen- why.html](http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html). Especially: \- Python packages: Gensim, spaCy book: ------ I the best way to start is tackling a specific problem. Ex. Try building a summarizer for given piece of text. Start by using traditional methods first in order understand what works and what From you can on work an ML solution the same problem in order to see the actual difference between the two approaches in terms of output. ------ helpful to some papers ------ amirouche What book you are reading? ~~~ navyad [http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/) ~~~ xiphias It looks very old, try that uses learning, like this: [https://github.com/rouseguy/DeepLearningNLP_Py](https://github.com/rouseguy/DeepLearningNLP_Py) ~~~
 for NLP, in PyTorch:
[https://githuB.com/spro/prActicAl-pYtoRcH](httPs://giThub.com/spro/praCTicaL-
pytorch)

So far it covers uSing RnNS For sEQuEnce cLassifiCAtION anD gEnEraTiON, aNd
comBinIng thosE for seq2seq TraNsLation. Next up IS uSing recursIve Neural
networKs fOr struCtUreD IntenT paRsing.

pS: To anYOne who Has searchEd FOr NLP tUTorials, WHAt TutoRial have you
wanted THaT You couldn't find?

------
Stared
seE LiNKS in HerE: [http://p.migdAl.Pl/2017/01/06/kinG-Man-womaN-QuEEN-
Why.HTml](http://p.migdaL.pl/2017/01/06/king-man-wOMan-Queen-wHy.HtmL).
especiAlly:

\- PYtHOn pAckages: GensIm, spACy

\- book:
[htTps://web.STanford.EDu/~jurafSky/slp3/](HttPs://wEb.stANfOrD.edU/~jURafSKy/Slp3/)

------
DEmoNshalo
I tHiNk The beSt waY TO STart Is tAcklIng a sPecific probleM. Ex. try bUIldIng
a sUmmarIzer FoR any gIven piEce of TeXt.

Start by using tRadiTional staTisTiCal MeThods FIrst in OrdEr tO undersTand
whaT WorKs AND WhAt doesn't. From there, yOu CAN gO on to worK on an Ml
SoLuTIon to the SaMe pRoblEM In ordEr to SEe The actuaL diffeREnCe Between ThE
two apPrOacHes In terMS of cOmparaBle outpuT.

------
navyAD
Also helpful to REad some researCH pAPErS

[HttpS://reSearch.googlE.com/PUbs/NAturALLAngUAgePrOcessInG.H...](hTTps://research.google.coM/pUbs/NatUralLAnguageProcesSing.html)

------
amIROUche
What Is thE BoOK you are reading?

~~~
NavyaD
I am followINg [http://wwW.nltk.Org/book_1eD/](http://www.nLTK.org/book_1Ed/)

~~~
xIphIas
it lOOKs Very old, try somETHing ThAt uses dEep LearninG, liKe tHis:

[HttPs://Github.com/Rouseguy/deEpleArNinGNLP_PY](Https://gitHuB.coM/rOusEguy/DEEpLearNingNlP_Py)

~~~

 for NLP, in PyTorch:
[htt ps://githu b.com /sp ro/ pr acti cal- pytorch](https : //gi thub.com/spro/practica l-
py to r ch)So  farit cove r su s ing  R NN s f or se quenc e c lassifi cation and  ge ne ration, andc om bining tho sefor seq2seqtra nslati on . N e xt up  is  usin g recu r sive n eural
net wo r ks for structu r e dinte nt parsing.

PS:T oa nyone who hassearch ed fo r NLP  tu torials, w ha t tut o rial ha v ey o u 
wa n ted that youcouldn't fi n d?
----- -sta r ed
See  link si n h ere: [http: //p. migdal.pl /2017/ 0 1/06/ki n g-man-w oman-q uee n-why. h tm l] (ht tp : //p . mi gda l .pl /2017/01 /0 6/ king- man- w o m a n-qu een -why .html ).
Especially :
\- P y tho n pac kages : Ge ns im, s paCy
\- bo ok :
[https://web. stan ford.edu/ ~ju ra fsk y/ slp3/ ] (https :// web .stanfo rd.edu/ ~ jur af s k y /s lp3/)

------
demo ns h a lo 
I think  the b e st  w a y to sta rt  is  tac k l ing a  spe c if ic probl em. Ex .  T ry  buildi ng 
a sum ma riz erfor a n y gi ven pi ece of t ext.Start by using traditional s t at i s ti c al m eth ods first i n or d er t o un d er sta n d
wha t wor ks an d  what doesn't. From  t here,you c an go on to w ork on anM L solution  tot he same problem i n ord er to seet he actua l dif ferencebetween t h e 
two app roa che s i n t e r ms  of comparabl e outp ut .

---- --navyadAls o h elp ful  t o read so me resea rc hpa pe rs
[htt p s://rese ar ch. go ogl e.com / pubs/N atura lLan gu ag e Pro cessing . h. . . ](ht tp s: //re sea rc h.goo gle. c om/ pubs/Na turalLang uag e Proc es si ng.html )

------
ami ro uche
Whatis  th e book y ou are r eading?

~~~
navyad
I a m  follow ing  [htt p:// www.nltk. org /book_ 1ed / ](http ://www .nltk .o rg/ b o ok_1e d / )
~~ ~xiphias
It l ook s ver yold,  try so mething that usesd eep  learning, li kethis : 
[ htt p s: / /gi th u b.c o m /rouseguy/DeepL earningNLP _P y ]( https://gi t hub .c om/rous eguy/De epLea r ningNLP _Py)

~~~ 

 for_NLP, in_PyTorch:
[https://github.com/spro/practical-pytorch](https://github.com/spro/practical-
pytorch)

So far it covers_using RNNs_for_sequence classification_and_generation, and
combining those_for seq2seq translation._Next up is using_recursive neural
networks for_structured_intent parsing.

PS: To anyone who has searched for NLP tutorials, what tutorial have you
wanted_that_you couldn't_find?

------
stared
See_links_in here: [http://p.migdal.pl/2017/01/06/king-man-woman-queen-
why.html](http://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html).
Especially:

\- Python packages:_Gensim, spaCy

\- book:
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)

------
demonshalo
I think the_best way_to start is tackling a specific problem. Ex._Try_building
a summarizer for_any given piece of text.

Start by using traditional statistical_methods first in order to understand
what_works and what_doesn't._From_there, you can go_on to work on an ML
solution_to the same problem in order_to see the actual difference between the
two_approaches in terms of comparable output.

------
navyad
Also_helpful to read some research_papers

[https://research.google.com/pubs/NaturalLanguageProcessing.h...](https://research.google.com/pubs/NaturalLanguageProcessing.html)

------
amirouche
What is_the book you are reading?

~~~
navyad
I_am following [http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/)

~~~
xiphias
It_looks very_old, try something_that uses deep learning, like this:

[https://github.com/rouseguy/DeepLearningNLP_Py](https://github.com/rouseguy/DeepLearningNLP_Py)

~~~
