3 ms·

I really love that this getting started guide is "do lots of studying and practice, here are the canonical textbooks, papers, conferences, tools, and problems"

by imh 8y ago

I really love that this getting started guide is "do lots of studying and practice, here are the canonical textbooks, papers, conferences, tools, and problems" instead of "spend a few hours on this superficial toy problem." I'd love to see more guides like this.

stared 8y ago
I strongly disagree. It's easy to list a lot of books and papers (and drown newcomers in them), without pointing to actual step=by-step starting points. Sure, doing superficial problems is only the first step (and it's foolish to think that it is the last step). Yet, you can read all books in the world, but unless you are able to prove theorems, or write code, you know less than someone who wrote a small script to predict names. Additionally, it's weird that they recommend NLTK (no, please not), SpaCy (cool and very useful, but high-level), but not Gensim, PyTorch (or at least: Keras). As a side note, PyTorch has readable implementations of classical ML techniques, such as word2vec (vide https://adoni.github.io/2017/11/08/word2vec-pytorch/ https://adoni.github.io/2017/11/08/word2vec-pytorch/). There are some good recommendations linked there (I really like "Speech and Language Processing" by Dan Jurafsky and James H. Martin https://web.stanford.edu/~jurafsky/slp3/ https://web.stanford.edu/~jurafsky/slp3/, and recommended myself in 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).
- throwawaymath 8y ago
  > Yet, you can read all books in the world, but unless you are able to proof theorems, or write code This is the implicit intent of reading all those books. If you actively follow along when learning from those books you'll be guided through plenty of those toy projects anyway.
- autokad 8y ago
  I finished top 25 in kaggle using NLTK and sklearn. word2vec is thrown around like the gospel in NLP, but simple techniques usually do a lot better because: #1 there isnt that much data in most cases and most importantly #2 the corpus differs substantially than the one word2vec was fit on. I am really flabbergasted by how many people start with word2vec and LSTM and come up with really over-fit models and they never even tried the simple things. using ngrams (1 and 2 on words, and 3-5 characters with truncated SVD) gets you really far.

by imh 8y ago

stared 8y ago

I strongly disagree. It's easy to list a lot of books and papers (and drown newcomers in them), without pointing to actual step=by-step starting points. Sure, doing superficial problems is only the first step (and it's foolish to think that it is the last step). Yet, you can read all books in the world, but unless you are able to prove theorems, or write code, you know less than someone who wrote a small script to predict names. Additionally, it's weird that they recommend NLTK (no, please not), SpaCy (cool and very useful, but high-level), but not Gensim, PyTorch (or at least: Keras). As a side note, PyTorch has readable implementations of classical ML techniques, such as word2vec (vide https://adoni.github.io/2017/11/08/word2vec-pytorch/ https://adoni.github.io/2017/11/08/word2vec-pytorch/). There are some good recommendations linked there (I really like "Speech and Language Processing" by Dan Jurafsky and James H. Martin https://web.stanford.edu/~jurafsky/slp3/ https://web.stanford.edu/~jurafsky/slp3/, and recommended myself in 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).

throwawaymath 8y ago
> Yet, you can read all books in the world, but unless you are able to proof theorems, or write code This is the implicit intent of reading all those books. If you actively follow along when learning from those books you'll be guided through plenty of those toy projects anyway.
autokad 8y ago
I finished top 25 in kaggle using NLTK and sklearn. word2vec is thrown around like the gospel in NLP, but simple techniques usually do a lot better because: #1 there isnt that much data in most cases and most importantly #2 the corpus differs substantially than the one word2vec was fit on. I am really flabbergasted by how many people start with word2vec and LSTM and come up with really over-fit models and they never even tried the simple things. using ngrams (1 and 2 on words, and 3-5 characters with truncated SVD) gets you really far.