my.glowing.paz.82
Region: KR
Tuesday 25 August 2026 03:58:47 GMT
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shirleyalexander53 :
Who sings this song? It is Beautiful 😍
2026-10-05 01:59:39
1
nila herrera :
belleza que melody🥰
2026-08-26 03:53:54
2
Leona Soles-Hilligus :
love this song
2026-09-22 11:25:10
1
Rhonda Armour :
So glad to hear it's not AI love this great song great voice
2026-08-27 05:28:45
9
Florence L.G. :
2026-10-04 00:07:06
1
An Ia :
♥️♥️♥️wspaniałego nowego tygodnia pozdrawiam ♥️♥️♥️
2026-09-27 20:42:43
2
Liz Fowler :
The title of the song is Stay in My Song Tonight
2026-08-26 04:48:31
7
Teresa Warren Cole :
Love this song
2026-10-02 14:01:15
1
An Ia :
wspaniałej niedzieli pozdrawiam ♥️♥️♥️
2026-10-03 20:59:09
1
An Ia :
witam cieplutko 💋
2026-09-02 20:26:51
2
An Ia :
♥️witam cieplutko
2026-08-31 20:52:06
2
An Ia :
♥️♥️♥️
2026-10-04 20:55:36
1
An Ia :
♥️♥️♥️
2026-09-30 20:25:12
1
An Ia :
♥️♥️♥️
2026-09-29 20:27:20
1
An Ia :
♥️
2026-09-23 21:11:22
1
An Ia :
2026-09-17 20:48:44
1
An Ia :
2026-09-14 21:14:56
1
An Ia :
♥️
2026-09-16 20:33:20
1
An Ia :
♥️pozdrawiam cieplutko
2026-09-11 20:34:41
3
An Ia :
wspaniałego weekendu ♥️♥️♥️
2026-09-11 20:45:28
2
鄺健富 :
good luck
2026-08-31 03:15:28
1
An Ia :
♥️witam cieplutko
2026-08-30 20:27:59
1
Mary Fedor :
beautiful song 🎵 ❤️
2026-09-28 23:03:10
2
An Ia :
♥️witam cieplutko ♥️
2026-09-10 20:33:25
1
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![If you’re learning Machine Learning, don’t memorize formulas blindly. Understand what each formula measures, when it is used, and what problem it solves. Here’s a practical ML formula cheat sheet 👇 📊 1. STATISTICS Mean μ = (1/n) Σxᵢ Variance σ² = (1/n) Σ(xᵢ − μ)² Standard Deviation σ = √σ² Z-Score z = (x − μ) / σ Covariance Cov(X,Y) = Σ[(xᵢ−μₓ)(yᵢ−μᵧ)] / n Correlation r = Cov(X,Y) / (σₓσᵧ) 🎯 2. PROBABILITY Conditional Probability P(A|B) = P(A∩B) / P(B) Bayes’ Theorem P(A|B) = P(B|A)P(A) / P(B) Independence P(A∩B) = P(A)P(B) 📈 3. LINEAR REGRESSION Prediction ŷ = β₀ + β₁x Multiple Linear Regression ŷ = β₀ + β₁x₁ + β₂x₂ + ... + βₚxₚ Mean Squared Error MSE = (1/n) Σ(yᵢ−ŷᵢ)² Root Mean Squared Error RMSE = √MSE Mean Absolute Error MAE = (1/n) Σ|yᵢ−ŷᵢ| 🔐 4. LOGISTIC REGRESSION Sigmoid Function σ(z) = 1 / (1 + e⁻ᶻ) Log-Odds log(p/(1−p)) = β₀ + β₁x Binary Cross-Entropy L = −[y log(ŷ) + (1−y)log(1−ŷ)] 📉 5. GRADIENT DESCENT Parameter Update θ ← θ − α∇J(θ) Where: α = Learning Rate ∇J(θ) = Gradient The goal is to move the parameters toward lower loss. ⚖️ 6. REGULARIZATION L1 / Lasso Loss = MSE + λΣ|wᵢ| L2 / Ridge Loss = MSE + λΣwᵢ² Elastic Net Loss = MSE + λ₁Σ|wᵢ| + λ₂Σwᵢ² 🌳 7. DECISION TREES Entropy H(S) = −Σpᵢlog₂(pᵢ) Information Gain IG = H(parent) − Σ(weight × H(child)) Gini Impurity Gini = 1 − Σpᵢ² 📏 8. DISTANCE METRICS Euclidean Distance d = √Σ(xᵢ−yᵢ)² Manhattan Distance d = Σ|xᵢ−yᵢ| Minkowski Distance d = (Σ|xᵢ−yᵢ|ᵖ)¹⁄ᵖ Cosine Similarity cos(θ) = (A·B)/(||A||||B||) 🎯 9. CLASSIFICATION METRICS From the confusion matrix: Accuracy (TP + TN)/(TP + TN + FP + FN) Precision TP/(TP + FP) Recall TP/(TP + FN) Specificity TN/(TN + FP) F1 Score 2 × (Precision × Recall)/(Precision + Recall) 📊 10. CLUSTERING K-Means Objective J = Σ||xᵢ−μcᵢ||² The algorithm tries to minimize the distance between data points and their assigned cluster centers. 🧮 11. PCA Covariance Matrix Σ = (1/n)XᵀX Eigenvalue Equation Σv = λv PCA finds directions that capture maximum variance. 🧠 12. NEURAL NETWORKS Linear Transformation z = Wx + b ReLU f(x) = max(0,x) Sigmoid f(x) = 1/(1+e⁻ˣ) Softmax P(y=i) = eᶻⁱ / Σeᶻʲ 🔥 13. DEEP LEARNING LOSSES Binary Cross-Entropy L = −[y log(ŷ)+(1−y)log(1−ŷ)] Categorical Cross-Entropy L = −Σ yᵢlog(ŷᵢ) Mean Squared Error MSE = (1/n)Σ(yᵢ−ŷᵢ)² 📐 14. OPTIMIZATION Momentum vₜ = βvₜ₋₁ + (1−β)∇J(θ) Adam Adam combines momentum-like first-moment tracking with second-moment tracking to adapt parameter updates. 🚀 THE ML MATH ROADMAP Statistics ↓ Probability ↓ Linear Algebra ↓ Calculus ↓ Optimization ↓ Machine Learning Algorithms ↓ Deep Learning 💡 Don’t try to memorize this entire list. Understand the intuition behind each formula and know where it appears in an ML workflow. 📌 Save this as your Machine Learning Formula Cheat Sheet. #MachineLearning #DataScience #ML #creatorsearchinsights #datascience](/video/cover/7688231672017620244.webp)
