Top Tweets for #MathForAI
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! π
#AI #MachineLearning #DataScience #MathForAI
π Day 27: AI/ML Journey
πΉ Math β Hessian Matrix = 2nd-order partial derivatives
πΉ Second Derivative Test β tells if a point is min, max, or saddle
Critical for optimization in ML π
#AI #MachineLearning #MathForAI #Optimization #MumInTech
π Day 25: AI/ML Journey
πΉ Math β Chain Rule = derivative of composite functions
πΉ AI β backbone of backpropagation in Neural Networks
No chain rule β no deep learning π‘π
#AI #MachineLearning #DeepLearning #MathForAI #MumInTech
π Day 24: AI/ML Journey
πΉ Math β Calculus
β’ Partial Derivatives β change w.r.t one variable
β’ Gradients β vector of partial derivatives, used in optimization
Gradients = the heartbeat of ML learning π
#AI #MachineLearning #MathForAI #Calculus #MumInTech
π Day 23: AI/ML Journey
πΉ Math β Calculus
β’ Continuity
β’ Derivatives
β’ Differentiation Rules (power, product, quotient, chain)
Derivatives = foundation of optimization in ML. Step by step π
#AI #MachineLearning #MathForAI #Calculus #MumInTech
π Day 22: AI/ML Journey
Didnβt do much today, but made a start with Limits (foundation of calculus & optimization in ML).
Not every day is heavy, but showing up matters π
#AI #MachineLearning #MathForAI #MumInTech
π Day 20: AI/ML Journey
πΉ Math β Statistical Distributions
β’ Gaussian (Normal) β bell curve, core to ML models
β’ Multinomial β outcomes w/ multiple categories (e.g., NLP word counts)
Distributions = backbone of probabilistic AI π
#AI #MachineLearning #MathForAI
π Day 19: AI/ML Journey
πΉ Math β Probability
β’ PDF β likelihood of values (for continuous variables)
β’ CDF β probability a variable β€ value
PDFs = shape of distribution, CDFs = accumulated probability π‘
Step by step π
#AI #MachineLearning #MathForAI m #MumInTech
π Day 16: AI/ML Journey
πΉ Review β LLN & CLT (more examples)
πΉ Math β Stats
β’ Hypothesis Testing
β’ Confidence Intervals
πΉ Python β Kaggle exercises (syntax, variables, numbers)
Theory + coding muscles, step by step π
#AI #MachineLearning #Python #MathForAI #MumInTech
π Day 15: AI/ML Journey
πΉ Math β Probability & Statistics
β’ LLN β sample mean β true mean as trials β
β’ CLT β sample means β Normal distribution π
These are the backbone of statistical inference in ML. Step by step π
#AI
#MachineLearning
#MathForAI
#MumInTech
π Day 14: AI/ML Journey
πΉ Math β Correlation
πΉ Weekly Review
β’ Probability basics
β’ Conditional Probability & Bayes
β’ Random Variables
β’ Distributions (Binomial, Poisson, Normal)
β’ Mean, Variance, Std Dev
Covariance & Correlation
#AI #MachineLearning #MathForAI
π Day 13: AI/ML Journey
πΉ Math β Statistics
β’ Covariance = how 2 variables change together
π +ve: rise together
π -ve: one rises, other falls
Couldnβt cover correlation today (busy w/ home + client work). Will continue tomorrow. Step by step π
#AI
#MathForAI
π Day 11: AI/ML
πΉ Math β Probability Distributions
β’ Binomial β successes in fixed trials
β’ Poisson β rare events
β’ Normal β bell curve π
πΉ Python β Loops
β’ for loop
β’ while loop
From math models to coding logicβstep by step π
#AI #MachineLearning #MathForAI
Day 10: AI/ML Journey
πΉ Math β Probability
β’ Discrete RVs β countable values (e.g., dice rolls π²)
β’ Continuous RVs β any value in a range (e.g., height π)
They form the basis of probability distributions in ML π‘ Step by step π
#AI
#MachineLearning
#MathForAI
π Day 9: AI/ML Journey
πΉ Math β Probability
β’ Conditional Probability
β’ Bayesβ Theorem (update beliefs w/ new evidence)
Abstract at first, but real-world examples (medical tests, spam filters) make it click π‘ Step by step π
#AI
#MachineLearning
#MathForAI
#MumInTech
πΉ Operators (Math, Comparison, Logical)
πΉ If/Elif/Else
πΉ Strings (Indexing, Slicing, Methods)
Step by step π
#AI #MachineLearning #Python #MathForAI
Day 16 of my AI Journey π
Today I explored Matrix Operations β the backbone of Machine Learning & Deep Learning!
π Addition, Multiplication, Transpose, Inverse
#100DaysOfAI #MachineLearning #MathForAI
π Day 5: AI/ML Journey
Motherhood comes first β€οΈ Today I spent most of the day caring for my sick child.
Still, while waiting at the hospital, I managed to cover:β¨
πΉ Math β Inverse of a 3Γ3 matrix
Not every day will be a big win, but consistency matters π
#MathForAI
π Day 4: AI/ML Journey
Todayβs progress:
πΉ Math β Determinants of 2Γ2 & 3Γ3 matrices + Inverses
πΉ Python β If, Elif, Else statements
Equations & logic β Stronger AI foundation π
#AI
#MachineLearning
#Python
#MathForAI
π Day 3: AI/ML Journey
Learned today:
πΉ Math β Matrix basics (Addition, Multiplication, Transposition)
πΉ Python β Strings (Indexing, Slicing, Methods)
Small steps, solid foundation π
#AI
#MachineLearning
#Python
#MathForAI
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