As far as the east is from the west so has he removed our transgression from us Psalm 103:12

Can you calculate a one-node neural network by hand ✍️

MIT's "Statistics for Applications" Lecture Videos: https://t.co/qivvGmn8mk Lecture Slides: https://t.co/RZXnnosERg

Game Theory Explained:

11 FREE Books from MIT for Absolute Beginners - Machine Learning (ML) - Deep Learning (DL) - Reinforcement Learning (RL) - Artificial Intelligence (AI) To get: - 1. Follow (So I can DM you ) 2. Like & retweet 3. Reply " Send "

This animation breaks it down—literally. What you’re seeing is how models convert human language into vectors in 3D space. Each word or phrase becomes a direction or position This is how machines “learn”: by turning language into math

weekend project status: still ‘in development’ since 2020.” 😅

🥲

pip install wife 😌

> roadmap to mathematics for machine Iearning

> who am i when nobody's watching

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this is it

omo the struggles 😂😂

okay this is tuff 😅💜

Crying guy: “Noo you can’t just solve the economy with programming!!” Chad coder: while True: print("GDP++")

Transformers have lower inductive bias, they learn patterns from data, not from built-in assumptions like locality or hierarchy Lower inductive bias = more flexibility, but more data needed to learn effectively.

MLPs (Multi-layer Perceptrons) can model complex functions but learn from data without strong built-in structure CNNs use locality and translation invariance (good for images)

Inductive bias = the assumptions a model makes to learn patterns from data. Linear Regression assumes linear relationships SVMs assume linear boundaries (unless using kernels) Decision Trees split orthogonally (axis-aligned) large language models