Hi, great presentation, thank you

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Compensating the creators of the training data might sound like a good idea, but it breaks down in a few key areas: -Generative models can/will be created/modified/used locally by individuals and small groups, not just large companies, making enforcement difficult/impossible. -If you go so far as to ascribe authorship rights to generated works, it creates issues for licensing and copyright expiration. Additionally, the creator of the first instance of that element has likely been dead for over 70 years, which suggests the element should be in the public domain. -If the same element of a generated work is tied back to thousands (or more) of individuals, you can argue that element isn’t substantial enough to warrant copyright protection due to a lack of uniqueness.

Given that her movies are publicly available, I think it's very likely that they just went data scraping after she denied their offer.

Shitty Dutch accent

Just checking whether Murilo keeps his word on answering the comments 🙃

this is not real time :(

Interesting thought process on whether we should care about writing dialect-inspecific SQL :) Maybe just embrace dialects and gain performance, clarity, usability, "you're not migrating everyday" - agree.

Thanks… 13:14 I have a question that why do you use precision, recall…. metrics (metrics for classification)? And how does model calculate that, because its not discrete value. I am a newbie

I think the lecturer misunderstands how pruning works. For it to be effective, there should be opportunity to continue or skip evaluation. In boosting or trees case, it's partial_fit. If you don;t utilize it, it does not matter that you receive ShouldPrune signal from the pruner: you are not exploiting it anyway, as you have already finished training & scoring. That's why with "pruning" the author had the same runtime as without it. Btw, Optuna docs suffer from initializing and partitioning data within the objective function. I don't understand why is everyone copying that without any thinking.

i know this comment is 2 years late, but does this work if i deploy my app to the internet, and it will access the users mic?

Can't run the code for tensorlow version greater than 1.15.0. How can I resolve this?

Very informative. Thanks Frederick it's a great presentation.

thanks when you run a test, the results do not look good. for those with 'interaction' equal to 1, the prediction should be close to 1. but this is not the case.

thanks. when you test, you are using data from training. i am referring to this line: long_test = wide_to_long( ) the parameter should be data['test'].. please correct me if i am wrong,

thanks. i see a problem with calling make_tf_dataset() just once for training. this function returns a size of 512 in tensor type. you are using this data just once for training. i think you need to put this into a loop. or make the batch size bigger. am i missing out in understanding?

ᎮᏒᎧᎷᎧᏕᎷ

Do we need historical data to train our mode in reinforcement learning?

My second question is, if interactions were not encoded as binary, but encoded as the actual ratings (explicit feedback rather implicit feedback), does your provided code still produce meaningful ncf_predcitons?

I did not really understand what these ncf_predictions means for the prediction. Does higher ncf_prediction value for specific (user_id,item_id) means they should be recommended to the user? Then, during the recommendation phase, for every (user_id,item_id) pair, should I recommend the item_id with the highest ncf value to that user?

dataroots- well.

very good insight!

Good demonstration. Thanks for sharing

Hi Vitale and everyone, great presentation so far, thanks for sharing this with me. Have you guys worked with Object Tracking models?

Nice guys ! You look like rock stars !

Thank you, loved the explaination, you covered quite a lot in very less time and also very clearly

Great content, thanks guys!

Hii! Do you any code related to real time interview app with streamlit and python

For me, this was the clearest explanation..!!

👍🏼

Sounds like a halflife song from the soundtrack

Great episode! Really enjoyed it!

starting 12 min there is no content please remove it

thanks for sharing

Very useful and cool. Thank you! We use S3 in our installation <---->DataSync<--->EFS mounted in ECS

hello,thanks for this video if u can pls send me the code plz

thanks.. I have question on userid information... is it possible to provide user related information as input to model?

Man, this sound quality is terrible. I can only understand maybe 20% of what you're saying. Even google captions think you're speaking German half of the time. Very disappointed.

Howdy, cowboys!

awesome I like it 😊 I have a question is there a way to put a debugger and then try some codes on the fly without rerun everything like in a notebook in python Thanks

nice explained video i don't find this path in github. can u plz help me with source link source = "datarootsio/ecs-airflow/aws"

Very good presentation.

Can you share the code you have showed during the demo to create the pipeline?

thanks , but the problem with these kind of videos is , you are talking to an expert guy who know all these things, but someone who does not know these things will not understand anything ! i hope in future videos be more detailed and slowly explain each steps not only read slides !

Where I find out the code showed in this demo/live? Could you share with us?

How can I convert speech to text using Streamlit ?

Do you modify the similarity distance by giving a pairwise constraint to the model? What do you modify exactly when you give this new constraint? (this is more a question about semi-supervised learning and not interactive clustering)

samuel samuel does whatever samuel does can he swing from a web? no he can't he's just a devops guy watch ouuuut here comes samuel