why are using r instead of python

What did you did for two years? I mean which course?

Welcome!

It's a huge field, and I'm just scratching the surface. I hope it's useful! :)

loooool I too had this feeling :D :D

Thanks!

Thanks, Matty!

Laurence Moroney, I have a specific tensor flow question regarding beautiful soup and specifically gathering text from an html output. Is there anyway we could start a dialogue?

We're happy to hear that the video was helpful. If you'd like to learn more about NLP, check out the NLP Zero to Hero playlist → goo.gle/nlp-z2h

@@TensorFlow I have checked it. But I have one request can we build a model like chatgpt using tensorflow 🤔

For 3 episodes, and I'm working on another 3 for text generation to come out in the not-too-distant future. I hope!

Glad you enjoyed!

Thanks, Mohamed!

Glad it was helpful!

what's used now?

But you got me instead ;)

ope -- looks like the more frequent you are, the smaller your assigned integer. Correct?

Yeah...that's a little more difficult in preprocessing text. I won't be covering that...sorry!

Those are sub-words, and a different tools can be used for obtaining them, such as sentencepiece (github.com/google/sentencepiece). In this case the model searches for common sub words such as play and in case of plays it tokenizes as . It is also possible to tokenize as the character and as a sub-word.

yup ofcourse. you can lemmatize these words or stem these words

Thanks!

did you got answer for this from any other source ?

@@vishnurajyadav8917 yes, we can control by changing filters parameter www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer

next week!

I can't compare with the others...but this way they're in a unified framework that makes it less code when I get around to training a NN with them (in episode 3)

The colab was adapted for one I wrote at Coursera, where it was course 3 in teh TensorFlow:In Practice specialization. There's more there. Otherwise, this is a 3 part series, with part 2 now on the YT channel :)

You need to find and download Azerbaijani corpus from the Internet. You can then prepare the word index using Tensorflow. The rest of the steps should be the same as the English example shown in the video. I don't know about the Azerbaijani language but some languages, like Tamil, don't have separate grammatical words like English. You need to make heaps of preprocessing before you prepare the word index. This is something you need to be aware of. Also, if you can't find a corpus for your language, use something called "hashing trick" (or "feature hashing") to hash the individual words in your language. Luckily, Tensorflow supports hashing trick.

It can be used for any language:)

Yep, came out yesterday, check yt.com/tf for details

i'm also wondering why this is the case, especially when i set num_words to be 1 or even 0 it still tokenizes all the provided words. have u got the answer for this?

Medogb Medo exact the same way when you’ve installed python and TensorFlow

:)

You can probably do decently well using just nouns, but you will probably also lose a lot of information if you filter out non-nouns at the Tokenization or pre-processing step. For example, if you only use nouns, you could very well pick up on a topic like "machine learning" in your dataset, but you might miss separate discussions of "deep learning," because "deep" is an adjective that would get filtered out and you would be left with just general "learning." An ultra-crude way you might augment this a bit is to instead do topic modeling on n-grams and keep only those n-grams that contain at least one noun, but I haven't tried this, so I can't assert it will actually work.

@@Aoitetsugakusha +1 Great answer

Depends what your objective is. If the end result is only centered around identifying entities then keeping NN/NNP may make sense(given your POS tagger is not making errors). It all depends upon the objective, for my use case I remembered I have extracted chunks of SVO phrases(Subject-Verb-Object) and then performed topic modeling, that had worked well for me, but I had made adjustments to my POS tagger to do this task well.

I've only tried English, but this technique should work with most languages. Try the notebook linked, and change the language and see what happens?

Thanks!

Шансон

"From first principles" means teaching with zero (or at least very few) assumptions

@@LaurenceMoroney sank u , I hope I can figure it out

‘From first principle’ could also mean from the smallest to the biggest:from the known to the unknown basically it’s a way of breaking concepts down to the simplest form

Beyond the obvious "it has one more word", there are several approaches. One that is fairly easy is to have a list of all words in a language with their connotations (this can be found online), one possible connotation being negation. Then, you can write code that inverts the connotation of a word if there is a word that implies negation near it.

If you have lots of sentences that are similar except for the word 'not', and label them accordingly. Then train a classifier like we do here, the 'not' would become a really strong signal towards the negative. Give it a try, instead of using the sarcasm dataset. The code would be very similar to this video.

...and what are these steps? With these videos and the codelabs, we'll have everything we need to build a simple text classifier, the beginnings of NLP.

@@laurencemoroney655 Maybe he's referring to the clean up required for the grammar (like someone pointed out: play vs plays)? However, Tensorflow cannot include that in the library as he's suggesting. Because Tensorflow is not an English-only library rather a more generic one.

Ha! I can only produce so many....

Even with these instructions? I've tried to make it as simple as possible and provided a colab to step through the code yourself. Don't know if it can be simplified any more.

@@laurencemoroney655 Its great. When we will get the next tutorial?

@@balachkhan1578 We're releasing them weekly

@@laurencemoroney655 TensorFlow can't be installed with Python 3.8. Will the issue be solved or i should switch to Python 3.7?

@@balachkhan1578 It's constantly being updated...so keep an eye on www.tensorflow.org/install. Right now it's up to 3.7 on there.

Even with these instructions? I've tried to make it as simple as possible and provided a colab to step through the code yourself. Don't know if it can be simplified any more.

@@laurencemoroney655 It is a bit complicated the first time. But taking up a small dataset/project for nlp and then revisiting the video again makes everything a lot more clearer. Plus you pick up on things that slipped your mind the first time :)