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!
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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]
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.
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