Top Tweets for #MathforAI
Matemática y Estadística para Inteligencia Artificial (IA)
⏱️ 3.3 hours
⭐ 4.81
👥 321
🔄 May 2025
💰 $14 → 100% OFF
https://t.co/dAGY0fBW9V
#AI #DataScience #MathForAI #udemy
Day 22–23 | Eigenvectors & Eigenvalues
Eigenvectors → directions unchanged by a matrix
Eigenvalues → how much they scale
Forms eigenspace/eigenbasis → used in PCA, dimensionality reduction & neural nets
#LinearAlgebra #Eigenvectors #Eigenvalues #AIEngineering #MathforAI
📈 Stats course ✅
Realized every AI “engineering” problem = math problem.
Cosine similarity → geometry
Confidence scores → probability
Agents → logic optimization
AI engineering = math + code.
Now, back to building 🚀
#MathForAI #BuildInPublic
LLMs loop on debug? Discrete math trick: List 10/20/30 causes (fit your token budget) sorted by likelihood. Zero iteration; fixing bugs faster--indie devs love it so do LLM architects. 💥 #LLMDebugHacks #MathForAI
3/
The chain rule breaks the loss derivative into simple parts.
No matter how deep the model is, it can compute dL/dW efficiently — one partial derivative at a time.
This is why deep learning scales.
#MachineLearning #MathForAI
5/
Small formula. Massive influence.
Loss functions are the quiet engines behind every model’s progress — from linear regression to billion-parameter LLMs.
#ML #AI #MathForAI
5/
Amazing how one tiny formula — just max(0, x) — became the backbone of billion-parameter models.
Small math. Massive impact.
#AI #MathForAI #ReLU
Wrapped up my learning on Probability & Statistics for AI/ML
Built a strong base in math for ML now ready to dive deeper into real AI projects! 🚀
#AI #MachineLearning #DataScience #MathForAI
"Efficiency? Real-world problems laugh at your neat numerical solutions."
https://t.co/qb8AZC9t07
#ConvexOptimization #MathForAI #STEM #OptimizationBook #AppliedMathematics
Convex Optimization (#1 best-seller in Linear Programming): https://t.co/TEICbT9Uqr
Comprehensive introduction to the subject, this book shows how such problems can be solved numerically with great efficiency. Solutions are applicable to many fields: engineering, computer science, mathematics, statistics, finance, and economics.
Ever wondered how machines make predictions from data?
Learn Linear Regression the simple way!
Watch now on Mutlu Learning Hub: https://t.co/NAJbGWuILs
#LinearRegression #MachineLearning #DataScience #AI #MathForAI #DataScience #Statistics #SupervisedLearning
Day 3 of #LakshayLearns
What I did:
✅ Completed lecture 2 of the Linear Algebra course by Gilbert Strang.
✅ Solved Rotate Image on leetcode & subtract operation on Codeforces.
#LearningInPublic #AI #MachineLearning #DSA #MathForAI #LakshayLearns
Math Behind Machine Learning https://t.co/UmHVv6YspA via @YouTube
#MachineLearning #MathForAI #DataScience #ArtificialIntelligence #AIRevolution #MathTutorials #TechEducation #DeepLearning
📘 Day 27: AI/ML Journey
🔹 Math → Hessian Matrix = 2nd-order partial derivatives
🔹 Second Derivative Test → tells if a point is min, max, or saddle
Critical for optimization in ML 🚀
#AI #MachineLearning #MathForAI #Optimization #MumInTech
Math has never been my strongest area, but I know it’s the foundation of #AI and #MachineLearning. So I’m committing to learn math that powers modern AI systems. It won’t be easy, but growth never is — and I’m excited for the journey ahead. 🚀📚 #LearningInPublic #MathForAI #Keep
📘 Day 25: AI/ML Journey
🔹 Math → Chain Rule = derivative of composite functions
🔹 AI → backbone of backpropagation in Neural Networks
No chain rule ➝ no deep learning 💡🚀
#AI #MachineLearning #DeepLearning #MathForAI #MumInTech
📘 Day 24: AI/ML Journey
🔹 Math → Calculus
• Partial Derivatives → change w.r.t one variable
• Gradients → vector of partial derivatives, used in optimization
Gradients = the heartbeat of ML learning 🚀
#AI #MachineLearning #MathForAI #Calculus #MumInTech
📘 Day 23: AI/ML Journey
🔹 Math → Calculus
• Continuity
• Derivatives
• Differentiation Rules (power, product, quotient, chain)
Derivatives = foundation of optimization in ML. Step by step 🚀
#AI #MachineLearning #MathForAI #Calculus #MumInTech
📘 Day 22: AI/ML Journey
Didn’t do much today, but made a start with Limits (foundation of calculus & optimization in ML).
Not every day is heavy, but showing up matters 🚀
#AI #MachineLearning #MathForAI #MumInTech
📘 Day 20: AI/ML Journey
🔹 Math → Statistical Distributions
• Gaussian (Normal) → bell curve, core to ML models
• Multinomial → outcomes w/ multiple categories (e.g., NLP word counts)
Distributions = backbone of probabilistic AI 🚀
#AI #MachineLearning #MathForAI
📘 Day 19: AI/ML Journey
🔹 Math → Probability
• PDF → likelihood of values (for continuous variables)
• CDF → probability a variable ≤ value
PDFs = shape of distribution, CDFs = accumulated probability 💡
Step by step 🚀
#AI #MachineLearning #MathForAI m #MumInTech
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