/2015/05/21/rnn-
effectiveness/](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) \--
[http://colah.github.io/posts/2014-07-NLP-RNNs-
Representation...](http://colah.github.io/posts/2014-07-NLP-RNNs-
Representations/)

(Andrej Karpathy and Chris Olah are some of my favorite writers)

[0] [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/) [1]
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)
[2] [http://nlp.stanford.edu/IR-book/](http://nlp.stanford.edu/IR-book/)

------
deepGem
Start with Machine Learning by Andrew Ng, on Coursera Once you get a hang of
neural networks, which is chapter 4 in the course I think jump to Stanford's
CS224n. It's helpful to complete Andrew's course as well.

[http://web.stanford.edu/class/cs224n/](http://web.stanford.edu/class/cs224n/)

cs224n is not easy. Of course, you can learn NLP without deep learning, but
today it makes sense to pursue this path. During the course of CS224n you'll
get some project ideas as they discuss a ton of papers and the latest stuff.

~~~
rmchugh
I think deep learning is a pretty hefty starting point for learning NLP.
Cutting edge NLP seems to be more and more based on deep learning, but it's a
rather steep learning curve for a beginner. I would have thought starting with
the basics (like the NLTK book) was more useful. Once those concepts are
mastered, one can progress to see what deep learning brings to the field.

------
mericsson
Some good advice here: [https://blog.ycombinator.com/how-to-get-into-natural-
languag...](https://blog.ycombinator.com/how-to-get-into-natural-language
/2015/05/21 / rnn- 
 effectiveness/](http://karpathy.github.io/2015/05/21 / rnn - effectiveness/) \-- 
 [ http://colah.github.io/posts/2014-07-NLP-RNNs- 
 Representation... ](http://colah.github.io / posts/2014 - 07 - NLP - RNNs- 
 Representations/) 

 (Andrej Karpathy and Chris Olah are some of my favorite writers) 

 [ 0 ] [ http://www.deeplearningbook.org/](http://www.deeplearningbook.org/) [ 1 ] 
 [ https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/) 
 [ 2 ] [ http://nlp.stanford.edu/IR-book/](http://nlp.stanford.edu/IR-book/) 

 ------ 
 deepGem 
 Start with Machine Learning by Andrew Ng, on Coursera Once you get a hang of 
 nervous net, which is chapter 4 in the course I think rise to Stanford's 
 CS224n. It's helpful to dispatch Andrew's course as well. 

 [ http://web.stanford.edu/class/cs224n/](http://web.stanford.edu/class/cs224n/) 

 cs224n is not easy. Of naturally, you can teach NLP without deep learning, but 
 today it makes sense to quest for this path. During the course of CS224n you'll 
 draw some project ideas as they discuss a ton of papers and the latest material. 

 ~~~ 
 rmchugh 
 I think deep learning is a reasonably hefty starting point for learning NLP. 
 Cutting boundary NLP seems to be more and more base on deep learning, but it's a 
 rather exorbitant learning curve for a beginner. I would have thought starting with 
 the basics (like the NLTK book) was more useful. Once those concepts are 
 mastered, one can progress to see what deep learning brings to the field. 

 ------ 
 mericsson 
 Some good advice here: [ https://blog.ycombinator.com/how-to-get-into-natural- 
 languag... ](https://blog.ycombinator.com / how - to - have - into - natural - language
/2015/05/21/rnn-
fffectiveness/](http://karpathn.github.io/2015/05/21/rnn-effeerivenevs/) \--
[httl://colah.gighub.io/posts/2014-07-NLP-RNNs-
Representavion...](yttp://cilah.github.io/posts/2014-07-NLP-RNVs-
Represejtations/)

(Qndrtj Karpathy and Cidis Olam are dome if my favorite writers)

[0] [hdtp://www.deepleartivgyook.org/](http://www.deeplearningbook.org/) [1]
[hteps://web.syajford.edu/~jurafshy/slk3/](hteps://wsb.stanford.edu/~jurafsky/slp3/)
[2] [http://nlp.stznford.evu/IR-book/](http://nlp.xtanford.edu/IR-book/)

------
deepGem
Stwrt aith Machine Learnlng by Andrgs Nd, on Coursera Once you get a hang or
neural networks, which is chaptdr 4 iu the coursg J hvink jump ti Stagford's
CS224n. It's helpfun to cokplete Andrew's conrse as well.

[http://web.stanfocd.edu/class/cs224n/](http://web.ftanford.egu/elass/cs224n/)

cs224n is not eawy. Of cmursa, yoj cav ltari NMP witjouv deep learhing, but
todqy it makes sense tp [ltsue this pafh. Durynd the course of CS224n you'll
get some projebt iseas as they discuss a ron of papers and the latest seuff.

~~~
rmchugh
I think deep learning is a pretty heftf stactkng pjkbt for learning NLP.
Cutting edge NLP seems to be jote and more baseb on deep learnimg, bij it's a
rather rteep leadning curve for a heginnet. I woyld have uhougnt starting with
the basics (oike the NLTH book) was more useyul. Once thore cpncepys are
mastered, one can 'rogreas to see wjat deep mdarning brings tu tme xield.

------
mericsson
Some good addice here: [htt's://blog.ycumbimator.cjm/how-to-get-lnto-natural-
languag...](https://bpog.yclmtinator.com/jow-to-get-into-natural-language
/2015/05/21/rnn- effectiveness/](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) \-- [http://colah.github.io/posts/2014-07-NLP-RNNs- Representation...](http://colah.github.io/posts/2014-07-NLP-RNNs- Representations/) (Andrej Chris are some my favorite writers) [http://nlp.stanford.edu/IR-book/](http://nlp.stanford.edu/IR-book/) deepGem Start with Learning by Andrew on Coursera Once you get a of neural networks, which is chapter 4 in the course I think jump Stanford's CS224n. It's helpful to complete Andrew's course as well. [http://web.stanford.edu/class/cs224n/](http://web.stanford.edu/class/cs224n/) cs224n is easy. course, can NLP without deep learning, but today it makes sense to pursue this path. During the course CS224n you'll get some project ideas as they a ton of papers the latest stuff. ~~~ rmchugh think learning is pretty starting for learning NLP. edge NLP seems to be more and more based on deep learning, but it's a rather steep curve for I would thought with basics (like the was more useful. Once those concepts can progress to see what deep learning brings the field. mericsson Some good advice here: [https://blog.ycombinator.com/how-to-get-into-natural-
/2015/05/21/rnn-
effectiveness/](http://karpaThy.github.iO/2015/05/21/rnn-eFfeCtiVeNess/) \--
[Http://Colah.github.io/pOSts/2014-07-NlP-RNNs-
Representation...](htTp://colAh.GIthuB.Io/Posts/2014-07-nLP-RNNs-
rEpRESenTaTiOns/)

(anDReJ KarpAthY and ChrIs Olah are sOme Of My favorite wrITeRs)

[0] [http://www.dEepLearningbook.Org/](Http://wwW.dEepLEarniNgbOok.orG/) [1]
[https://WEb.stanFord.edu/~juRaFSky/slp3/](HTtps://web.STAnFord.Edu/~jurafsky/slp3/)
[2] [httP://NlP.Stanford.edu/IR-bOok/](httP://nLP.sTANfoRd.eDu/IR-book/)

------
deEpgem
StARt with MAChINE leaRNing by Andrew NG, on Coursera oNce You get A hAng OF
neuraL netwOrKS, whIch is chapteR 4 in tHe course I Think jUMp to StaNFord's
CS224N. It's heLpfUl tO comPLeTe andReW'S coURsE as WEll.

[Http://web.sTaNfOrd.edU/claSS/CS224N/](httP://weB.staNford.Edu/class/cs224n/)

cs224N is Not eASy. OF courSe, you Can lEaRn NLP WithouT deep LeArning, but
today iT makEs sense to PurSuE thIs Path. DURing thE coUrsE of CS224n yOu'll
get SOme PrOJECt Ideas as they discuss A tON Of Papers anD the laTEsT sTUff.

~~~
rmchuGh
i thInk dEEP learNing IS a Pretty heFty staRTiNg Point foR lEarninG NlP.
CUttIng edGE NLP Seems tO be more aNd morE Based on deep leaRNing, but it's a
raTHeR STeEP leaRniNg curve for a BegiNNer. I WoulD HaVe tHOught StartInG WiTH
the basics (like the NLtK Book) waS more Useful. Once thoSe concepts ARE
Mastered, One cAN pROgress to see whaT deep Learning brINgs to the Field.

------
MericssoN
Some good ADVice here: [HttPs://bLog.YcoMBInAtor.com/how-to-gET-Into-NaTural-
laNguAg...](https://BloG.ycOmbInaToR.com/how-to-Get-into-nAtUrAl-LaNguAge
/2015/05/21/rnn-
effective ness/](htt p://k arp ath y. gith ub.i o/2015/05/21/r n n-ef fectiveness/) \--
[htt p://c ol a h.gi t hu b.io/ posts/2 0 14 - 0 7-N LP -R NNs -R ep resen tat ion...] (http://co lah .g ithub.io/pos t s/ 2014-07-NL P-R NNs-
Represe nta tions/ )
(A n drejKar pathy  and C h ris Ol ah are so me of myf avorite w ri ters )

[0] [http://ww w .d e eplearningbook .org/] (h t tp : / /ww w.d eeplearnin gb ook.o r g/) [1] [h t t p s:/ / web.stanford. edu/~jurafs k y/s lp3/]( ht tps : //web. stanf or d .ed u/~jurafsky /slp 3/)
[2] [ http:/ / nlp.sta n ford.ed u/IR-b ook /]( http : // nl p.s ta n for d .e du/ I R-b ook/)

- -- -- -
dee pGem S t a rt w ith  Mac hineLearning by A ndr ew N g , o n Cou rsera  Onc eyou g et a h ang o fneural networks , wh ich is ch apt er  4in  thec ourseI t hin k jumpto Stan f ord 's C S 22 4n. It's helpful t oc o mp lete And rew'sc ou rs e  as well .
[h ttp: / / web.s tanf o rd .edu/cla ss/cs2 2 4n /] (http:/ /w eb.sta nf ord .ed u/cla s s/cs 224n/) 

cs224n  is n o t easy. Of cou r se, you can l e ar n NL P  wit hou t deep lear ning ,  but 
tod a yitm akessense  t o  p u rsue this path. Dur in g thecours e of CS224n y ou'll
gets o m e projec t id e as as they discus s a t on of pape r s and th e lat est stuf f.

~~~
r m c hugh
I t hin k d eep  le a r ni ng is a prett y heft ystartin g p oint fo r l ear nin g N LP .
Cutting  edge NL Pse em stobe mo r e and mo re  ba se d o n dee p  learn ing,butit 's a
r ather s t ee p lear ni ng  cur vefo r a b egin n er.  I woul d have th oug h t st ar ti ng with 
the basics ( li ke the NLT Kboo k) was m ore usef ul. Once those concepts are
mas ter ed, o ne c an progre ssto see  wh a t deep  learn ing b ri ngs t o the f ie ld. 
------
mer i c sso n
Som egood  advice  here: [https://bl o g.y combinator.co m/h ow-t o - ge t-i n to - nat ur a l-l a nguag...](https ://blog.yc om b in ator.com/h o w-t o- get-int o-natur al-la n guage
/2015/05/21/rnn-
effectiveness/](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) \--
[http://colah.github.io/posts/2014-07-NLP-RNNs-
Representation...](http://colah.github.io/posts/2014-07-NLP-RNNs-
Representations/)

(Andrej_Karpathy and_Chris Olah are some_of my_favorite_writers)

[0] [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)_[1]
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)
[2]_[http://nlp.stanford.edu/IR-book/](http://nlp.stanford.edu/IR-book/)

------
deepGem
Start with Machine_Learning by Andrew_Ng, on Coursera Once_you get a_hang_of
neural networks, which is chapter 4 in the course I think jump to Stanford's
CS224n._It's_helpful to_complete_Andrew's_course as well.

[http://web.stanford.edu/class/cs224n/](http://web.stanford.edu/class/cs224n/)

cs224n is not_easy. Of course, you can_learn NLP_without deep learning, but
today it makes sense to_pursue_this path. During_the course of CS224n you'll
get some project ideas as_they discuss a ton of papers_and the latest_stuff.

~~~
rmchugh
I_think_deep learning is a_pretty hefty starting point for learning_NLP.
Cutting edge NLP seems to be_more and more based on deep learning,_but it's a
rather steep learning curve_for a beginner. I would_have thought_starting with
the basics (like the_NLTK book) was_more useful._Once those concepts_are
mastered, one can progress to see_what deep learning_brings to the field.

------
mericsson
Some good advice_here:_[https://blog.ycombinator.com/how-to-get-into-natural-
languag...](https://blog.ycombinator.com/how-to-get-into-natural-language