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@wanmiraclee: gatauk males #fyp #nfurcounter #4u @yanjiegibobo

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wnnmrçyy
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Thursday 08 October 2026 03:19:13 GMT
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rrcyyrbl
›rraacYys4+ :
mojang Priangan
2026-10-08 05:01:34
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mynameisgope
gope tzy :
apa kabar wan
2026-10-08 03:35:32
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2026-10-08 04:56:05
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￴ 𝙕𝘼𝙆𝙓 | 𝐂𝐀𝐒𝐓𝐔𝐒🇦🇱 :
2026-10-08 03:57:47
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oke cil
2026-10-08 03:26:55
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#🐆🐆 @ℒ•💋
#🐆🐆 @ℒ•💋
Before training ML models, we often need to scale features so that no variable dominates others. One popular method is Normalization. ⚖️ 🔹 What is Normalization? 👉 Normalization rescales data to a fixed range, usually [0, 1] (sometimes [-1, 1]). It doesn’t change the distribution shape, just the scale of the values. 🔹 Why do we need Normalization? Consider a dataset with: • Age: 20 – 70 • Income: 15,000 – 200,000 ➡️ Without normalization, models like KNN or Gradient Descent will give more weight to Income just because numbers are bigger. ✅ Normalization ensures all features contribute equally. 🔹 How does it work? Formula (Min-Max Normalization): X’ = \frac{X - X_{min}}{X_{max} - X_{min}} Where: • X = original value • X_{min}, X_{max} = min & max of the feature Result → Value between 0 and 1. 🔹 Example: 📊 Suppose exam scores = [50, 70, 90] • Min = 50, Max = 90 ➡️ Normalized 50 = (50-50)/(90-50) = 0 ➡️ Normalized 70 = (70-50)/(40) = 0.5 ➡️ Normalized 90 = (90-50)/(40) = 1 Now all scores are in the same scale (0–1). 🔹 When to Use Normalization? ✅ Best when data does not follow a Gaussian (normal) distribution. ✅ Used in algorithms like: • Neural Networks (fast convergence) • K-Nearest Neighbors (distance-based) • Gradient Descent 🔹 Difference between Normalization & Standardization • Normalization → Rescales data to a range (0–1) • Standardization → Centers data around 0 mean & unit variance ✅ Takeaway: Normalization makes all features fit within the same scale so that ML models treat them equally and training becomes more stable. #MachineLearning #Normalization #DataScience #Preprocessing #100daysofml
Before training ML models, we often need to scale features so that no variable dominates others. One popular method is Normalization. ⚖️ 🔹 What is Normalization? 👉 Normalization rescales data to a fixed range, usually [0, 1] (sometimes [-1, 1]). It doesn’t change the distribution shape, just the scale of the values. 🔹 Why do we need Normalization? Consider a dataset with: • Age: 20 – 70 • Income: 15,000 – 200,000 ➡️ Without normalization, models like KNN or Gradient Descent will give more weight to Income just because numbers are bigger. ✅ Normalization ensures all features contribute equally. 🔹 How does it work? Formula (Min-Max Normalization): X’ = \frac{X - X_{min}}{X_{max} - X_{min}} Where: • X = original value • X_{min}, X_{max} = min & max of the feature Result → Value between 0 and 1. 🔹 Example: 📊 Suppose exam scores = [50, 70, 90] • Min = 50, Max = 90 ➡️ Normalized 50 = (50-50)/(90-50) = 0 ➡️ Normalized 70 = (70-50)/(40) = 0.5 ➡️ Normalized 90 = (90-50)/(40) = 1 Now all scores are in the same scale (0–1). 🔹 When to Use Normalization? ✅ Best when data does not follow a Gaussian (normal) distribution. ✅ Used in algorithms like: • Neural Networks (fast convergence) • K-Nearest Neighbors (distance-based) • Gradient Descent 🔹 Difference between Normalization & Standardization • Normalization → Rescales data to a range (0–1) • Standardization → Centers data around 0 mean & unit variance ✅ Takeaway: Normalization makes all features fit within the same scale so that ML models treat them equally and training becomes more stable. #MachineLearning #Normalization #DataScience #Preprocessing #100daysofml
tag dc please🥺
tag dc please🥺
done @rasya.ajaa3 #my #goldlaner #ml
done @rasya.ajaa3 #my #goldlaner #ml


shala baba na marn kisay da #😭💔😭💔😭💔#repost #like #foryou
shala baba na marn kisay da #😭💔😭💔😭💔#repost #like #foryou

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