You're closer but not yet quite there - the smaller Google News Dataset W2V is trained on is 10 GB. The full one used is around 300GB IIRC

I've heard a lot of people saying the same thing but could you explain why PyTorch is better than TF?

I will check Gensim documentation. Thank you

I think trascript from company's conference call includes some certain characterstics since business professionals may use some particular verbs or expressions. I haven't checked out w2v datasets you mentioned yet. Is there existing corpus that's business-oriented?

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What if dataset size increases to 1 giga bytes. Is it big enough?

Gensim documentation itself has them highlighted along with the necessary arguments to use to download and use them.

VAD won't do it, since the speech and the noise overlap

This.

Thanks for the info. I knew PyTorch was popular, but didn’t know about TF’s waning popularity. Good to know.

TF out, Pytorch in. Google always struggled to build a long lasting ecosystems for their products. Angular was first but then Facebook came and swooped in with React, same happened with TF and pytorch

PyTorch is where it’s at !

Can you say more about “on its way out?” Are other libraries more popular in your opinion?

This how a Chinese person I know speaks.

Which reasons :D

I read this in a funny Chinese accent for some reason.

Likely not enough, at least not for what is considered good. But I fail to see why you'd want to trian it yourself, there are plenty of readily available w2v weights or vocabularies.

TF is on its way out. But in general get certificates for fundamentals not for frameworks that change every couple of years. Certificates are popular in ops domain, so if you are planning on doing mlops then get something related to that.

Imo not really, from what I've heard and seen most jobs don't really care for random certs from courses. You can just learn everything the course will teach from free resources so you can just search online for the topics you're interested in.

I tried levenshtein distance algorithm but it's not works like I want

Do you absolutely need to use deep learning? There are tons of way faster autocorrect implementations that use levenshtein distances and non-DL techniques such as SymSpell or Norvig’s algorithm. DL is complicated, expensive, and requires tons of data to train on - I would stay away from that unless you’re doing it for your own enrichment or a school project.

You could try something that works on a character level, like a sequence tobsequence model, or maybe a rnn approach like char2vec. Both approaches should work pretty good if you have enough training data

I meant on the betting site

Ram performance also may be affected by meltdown, spectre patches.

Simulate? How would I go about doing that? Sorry if that's a silly/involved question, but I'm not sure how I would simulate NBA games.

You can always simulate

Haha, I live in CA so sports gambling so that's out of the question...

I was actually hoping to maybe write a paper or something and submit it to something like the Sloane conference or send it in to 538 as an add-on to my resume?

Also, my model uses data from seasons going back all the way to 2014 as of right now. Larger datasets would make a better model, right? So why not use more historical data?