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Why Gauss Was Truly Great Newton didn’t just "see" gravity, and Gauss didn’t just "invent" Least Squares. Their genius was in completing the logical chain that no one else could. 🔗 Learn how Gauss
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Gauss’s Idea Behind Maximum Likelihood In 1809, Gauss finally completed the idea he had been working toward. In Theoria Motus, he showed that each observation has a probability of occurring. When we
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Why the Normal Distribution Makes Least Squares Optimal In 1809, Gauss finally answered the question he couldn’t fully justify in 1801. In Theoria Motus, he explained why least squares — what we now
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In 1801, Gauss Fit Models Like We Do — Without Knowing Why In 1801, Gauss calculated the orbit of a lost planet using only 24 noisy observations — with no computers, no modern theory, just pen and p
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How Gauss Solved a Planet’s Orbit with Just 24 Noisy Points In 1801, astronomers lost track of a newly discovered planet: Ceres. Only 24 noisy observations remained — and no existing method could pre
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Gauss, Least Squares, and the Letter That Started a Fight When Gauss published Theoria Motus, he claimed that he had been using the method of least squares since as early as 1795. This naturally caus
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Why Squared Error? Gauss Had an Answer In 1809, Gauss finally answered a deep question in Theoria Motus. Why should we minimize the sum of squared errors? Why not minimize the sum of absolute errors
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Bell Curves, Squares, and a Solvable Problem To understand Gauss, we need to rewind. Long before Gauss, mathematicians were already noticing patterns. In 1733, de Moivre observed that a bell-shaped c
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24 Observations. No Orbit. Until Gauss. In 1801, a new planet was discovered — and then suddenly lost. After just 40 days, Ceres disappeared behind the Sun. All that remained were 24 noisy observat
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Before MSE, There Was Human Intuition about average ======================== All animations in this video were made with ManimGL and ManimCE, an open-source animation library built in Python. You ca
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Why Do We Use MSE Instead of MAE? We use MSE to fit a line — through the normal equation or gradient descent. But why do we square the error at all? If MAE feels more intuitive, what’s the real reason
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Why do we minimize mean squared error? In 1801, Gauss predicted a lost planet using just 24 noisy data points. That same idea still powers modern machine learning today. ========================All an
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About @syailect

Jay K is a Instagram growing creator. With 327 followers and 12 posts, syailect's account shows a follower-to-following ratio of 27.3:1. View all content anonymously on Pictame - no login required.

syailect's engagement rate sits at 1.27%. For accounts with 1K-10K followers, the average is around 5%, so there's some room to grow — though every community is unique.

Out of the last 12 posts, 100% are videos and 0% are photos. New content goes up pretty much every day — a very consistent posting habit. Captions lean detailed, averaging 1048 characters.

1.27% engagement rate — room to grow compared to average for 1K-10K follower accounts.
Posts every day — very consistent.
27.3:1 follower-to-following ratio.

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@syailect

Jay K

Public
Posts
12
Followers
327
Top Hashtags
#manim#MSE#MachineLearning#Gauss#LinearRegression#AI#SSE#MLIntuition

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

Following
12
Avg. Likes
4
Avg. Comments
0
Engagement
1.22%

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