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Thank you Dr. Frances for explaining how there are 100's of use cases of AI in telecom sector.

Can you please mail the slides

nice!

who’s better: nessi, hive ?

Hi Matt and Tom, great presentation. If you guys see this, can you please post the link to the blog that Tom mentioned at the end of this video. I think he wrote a blog about brining JSON into Iceberg and building analytics on top of that using Trino. Im planning to do a project so could use some help. Thanks!

Amazing information. Thanks for sharing. We are trying to build something similar.

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Sir I can help to increase your channel views?

Good content ❤❤

One of best data science channel really connect with real world Thank you so much

*PromoSM*

This is one of the best content I've seen on Data Science in my 3years of Data Science fellowship... Ma'am, your content is Master Class... thank you greatly for putting this out.

😉 *Promo sm*

00:29:30 Monadic ∪ is Unique 00:30:15 Depth is the level of nesting: arrays of arrays of arrays, etc. (The dimensionality is the number of dimensions: ≢⍴) 00:32:55 This usage of / is actually the monadic Reduce higher-order function (Replicate is also / but with a value on its left) 00:37:05 */⍳5 should really give 1 but on the way to getting that result, we have to compute a number with over 10⁴⁸⁸ digits and since that cannot be represented, we get a "domain error" 00:39:40 This usage of ⍨ is informally called Swap, while the higher-order function as a whole is called Commute. (Selfie is the informal name when there's only one argument, e.g. +⍨5 gives 10 because +⍨ is "plus selfie".) 00:59:45 1*2*3*4*5 is NOT equivalent to 1*(2*3*4)*5 because 1*2*3*4*5 is 1*(2*(3*(4*5))) - 4*5 is 1024 and 3*1024 is already 3.7×10⁴⁸⁸. Now, raising 2 to that power give you that enormeous number with over 10⁴⁸⁸ digits! 01:06:15 ⎕CSV can actually separate the header and parse the numbers: (e_4ch header)←⎕CSV'emo/4chan.csv'⍬41 01:15:00 You need to give ]plot a vector of two vectors, not a two-column matrix: ↓⍉e_4ch[;9 12]

What is this ?

Thanks for the workshop. I've already asked these but in the other video on the conference. I have some questions regarding Anomaly Detection and I hope Hasan or anyone else can answer it. 1. Do I need to train my anomaly detection model with normal data only? 2. What is the validation and testing data? Should I need to have normal data only or mix them with anomaly. If mixing is the answer what is the splitting ratios? Thanks and appreciation for the answers.

Thanks Hasan for the workshop. You are good in time series could you please make a series about GAN for time series> It will be great.

I love this talk!

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😈 𝘱𝘳𝘰𝘮𝘰𝘴𝘮

The woman is just reading a script! What a horrible presentation...

How can Data Scientists play a vital role in fighting against Cybersecurity Threats Provide an example.

Delivered with clarity and is substantive.

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Fantastic speech! Engaging, interesting, and exciting.