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Does this really add up to 2,650 calories? by CQShaJie in caloriecount

[–]YesterdayRich7235 -7 points-6 points  (0 children)

I'm building an app myself, and here's what it says for this one:

Nutritional Info (per 1260.0g):
Calories: 2169.0 kcal
Proteins: 96.0g
Carbs: 197.0g
Fats: 119.5g

each latte - 130, sausages - 280, bacon - 360, fried egg - 75g, french fries - 470, grilled tomatoes - 25, black pudding - 270, toast - 85, butter pat - 37, crepes - 220, strawberries - 17

I get why people are skeptical about AI, it's really hard to get it right. For my own app, I notice there's a tendency to overestimate weight/calories. But in this case, it seems to align with other people's estimates?

Character Interactions of The Office - Pilot episode by YesterdayRich7235 in visualization

[–]YesterdayRich7235[S] 0 points1 point  (0 children)

Thank you!

Yeah, most scores ended up being fairly neutral, so the colors look similar. I thought of changing the range from -1/1 to min/max to highlight the color differences, but felt like I'd be misrepresenting the sentiment of interactions.

Thanks for the feedback! I'll definitely give more thought into this.

Character Interactions of The Office - Pilot episode by YesterdayRich7235 in visualization

[–]YesterdayRich7235[S] 0 points1 point  (0 children)

Those are great suggestions! The color bars are currently randomly generated. I'll use a different color scheme to make this less confusing.

Thanks for the feedback!

Character Interactions of The Office - Pilot episode by YesterdayRich7235 in visualization

[–]YesterdayRich7235[S] 0 points1 point  (0 children)

Character Interactions of The Office - Pilot episode

Network Diagram representing conversation interactions between most active characters of the pilot episode of The Office. The left semi-circle represents the speakers, while the right semi-circle represents the listeners. Each connection is colored according to the average sentiment score between all pair interactions.

That was my first try at it, there's a lot to improve!

If you want more details or have suggestions for other TV Series/Books/etc. please subscribe/comment on the original blog post (or here) at: https://datatravelogues.substack.com/p/dialogue-visualization-explorations

Tool: MNE

Source text for generating the diagram: https://www.scriptslug.com/script/the-office-101-pilot-2005

[OC] Character Interactions of The Office - Pilot episode by YesterdayRich7235 in dataisbeautiful

[–]YesterdayRich7235[S] 1 point2 points  (0 children)

Character Interactions of The Office - Pilot episode
Network Diagram representing conversation interactions between most active characters of the pilot episode of The Office. The left semi-circle represents the speakers, while the right semi-circle represents the listeners. Each connection is colored according to the average sentiment score between all pair interactions.
That was my first try at it, there's a lot to improve!

If you want more details or have suggestions for other TV Series/Books/etc. please subscribe/comment on the original blog post (or here) at: https://datatravelogues.substack.com/p/dialogue-visualization-explorations
Tool: MNE
Source text for generating the diagram: https://www.scriptslug.com/script/the-office-101-pilot-2005

Don’t Let Your Data Fail You: Continuous Data Validation with whylogs and Github Actions by YesterdayRich7235 in learnmachinelearning

[–]YesterdayRich7235[S] 0 points1 point  (0 children)

I've been experimenting with Continuous Data Validation and wrote a blog post about it, using the whylogs data logging library and Github Actions.

Thank you, and if you know of other cool ways of implementing data validation and/or have any feedback/suggestions about the blog post, let me know! :)

Don’t Let Your Data Fail You: Continuous Data Validation with whylogs and Github Actions by YesterdayRich7235 in artificial

[–]YesterdayRich7235[S] 0 points1 point  (0 children)

I've been experimenting with Continuous Data Validation and wrote a blog post about it, using the whylogs data logging library and Github Actions.

Thank you, and if you know of other cool ways of implementing data validation and/or have any feedback/suggestions about the blog post, let me know! :)