Benjy Bodner

What is the main advantage of few-shot prompting over zero-shot?

5 days ago | [YT] | 2

Benjy Bodner

Not bad nano banana pro👏

1 month ago | [YT] | 6

Benjy Bodner

A bit too much text, but it summarizes the video nicely:

https://youtu.be/MtrSccrG6QI?si=oWJVY...

1 month ago | [YT] | 2

Benjy Bodner

Not bad, I think I agree

1 month ago | [YT] | 3

Benjy Bodner

Should You Micromanage? Well, It Depends…

Depends on what?? see here on my medium piece ----- > medium.com/beyond-agile-leadership/should-you-micr…

1 month ago | [YT] | 3

Benjy Bodner

What is the main problem with standard ReLU that led to many of its variants?

FOR MORE: Why Are There So Many ReLU Variations? medium.com/gitconnected/why-are-there-so-many-relu…

1 month ago | [YT] | 6

Benjy Bodner

Micromanagement — helpful tool or leadership trap?

1 month ago | [YT] | 3

Benjy Bodner

Is your ML model confused? 😵‍💫 Badly scaled features are the hidden problem sabotaging your performance!

Learn the two simple, essential techniques (Standardization & MinMax Normalization) that professional data scientists use to instantly align feature ranges and get a significant performance boost from your models. I'll show you the exact Python code you can use right now.

3 months ago | [YT] | 2

Benjy Bodner

You are given a dataset with 3 features: X1, X2, X3, each with 5 observations. You calculate the covariance matrix manually in Python. Here’s what you know:

The diagonal entries are all positive.

The covariance between X1 and X2 is 0.

The covariance between X2 and X3 is negative.

Based on this information, which of the following statements is true?

Warch it: https://www.youtube.com/watch?v=r9v_h...

Hint: Remember:

Diagonal entries = variance of each feature

Off-diagonal entries = covariance between two features

3 months ago | [YT] | 2

Benjy Bodner

In linear regression, what is the main purpose of adding a column of ones to the feature matrix X before applying the normal equation?

3 months ago | [YT] | 4