@wiilst.yr__: Cả đời em hong dám mong #kaitokid #detectiveconan #wing_grpᥫ᭡ #awaariisgr #seekersgr #yrislgrp #wlsgrp #ad🐧_squad🌀 #wi_irn #wiscl #crwth8 #fyp #viral #edit

𝘸𝘪ֹ⑅᜔
𝘸𝘪ֹ⑅᜔
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Region: VN
Monday 07 July 2025 10:00:58 GMT
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elthinmie
bị buồn ngủ 😪 :
Đẹp trai, đúng thật là đẹp trai, không còn gì để nói ngoài hai từ “đẹp trai”. Đẹp trai từ ánh mắt đến nụ cười, đẹp trai từng góc cạnh. Đẹp trai đến nỗi đứng yên cũng thấy đẹp trai, cười nhẹ thôi cũng đẹp trai, thậm chí im lặng cũng toát lên cái đẹp trai không tả nổi. Đẹp trai đến mức mỗi lần nhớ lại là chỉ biết nghĩ đến chữ “đẹp trai”. Đẹp trai kiểu khiến người ta không thở nổi, đẹp trai đến ám ảnh. Tóm lại, chỉ có thể kết luận một điều là đẹp trai.
2025-07-08 14:34:29
227
anh26012013
Kudo Skibidi :
cần ng xem vd
2025-07-24 06:15:39
0
banhbao_sicula
Banhbao sicula🐡 :
Kid mà có thật thì nyc làm gì có cửa với t🥰
2025-07-28 15:15:11
0
xfel.tn_
𝘛𝘯𝘢𝘩°ᝰ.ᐟ :
intr nó chiếm gần 70% cái video v
2025-07-07 10:20:19
487
kreshkhoaitomita
Simp be ki chayy :
beautiful, pretty, beauty, effortless, opulent, unparalleled, glamorous, magnificent, bewitching, immaculate, spectacular, exotic, bewitching, radiating, coveted, commanding, unrivaled, supreme, unassallable, unmatched, resplendent, enthralling, spellbinding, enigmatic, distinguished, unfading, irresistible, striking, vivacious, regal, impressive, majestic, ethereal, unforgettable, incomparable, breathtaking, sumptuous, elegant, resplendent, radiant, alluring, enchanting, ethereal, elegant, gorgeous graceful, unrivaled, graceful, mesmerizing, impeccable, charming, ravishing, sophisticated, bewitching, authentic, exceptional, sophisticated, sublime, opulent, gorgeous, awe-inspiring, awesome, statuesque, exquisite, alluring, prettiest, dazzling, effortless, cutest, perfect, princess, supreme
2025-07-24 03:48:49
2
ngwthz.soct
kameow :
Aoko sướng số 1 😭
2025-07-26 13:27:36
3
_cuoibanhlon
chu :
kid mà là con người tui cưới kid luôn
2025-07-19 10:52:06
55
ttramanhhuongnoi
Nào giàu đổi tên 🫵🏻 :
Ảnh mà có thật chắc cũng không xứng với ảnh 😂
2025-07-15 15:47:57
16
zzx_zh09
𝐩𝐭˚♬ :
shop có ảnh kid hk cho xin vs ạ
2025-07-24 05:38:40
0
fan.account_1412
fan account :
sốp ơi cho em xin đăng nhật kí đc k ạ
2025-07-24 08:45:13
1
me_bl_vaiii
thùy trâm >< :
xin vid up str ạa
2025-07-08 13:40:57
1
_rabbit.bie_
𝐫𝐚𝐛𝐛𝐢𝐭. :
bạn này ed chỉ có xịn
2025-07-08 05:49:10
1
sa922759
Blue 🧸 :
tiếc thật , ng tôi thích lại chẳng có thật
2025-07-13 16:54:31
43
shdnt_kw_mr
Vĩnh :
nghe nhạc sao thành "Cả đời em không rớt môn, cả đời em không rớt môn" 😞
2025-07-20 13:36:50
4
tdung_14109
??? :
đã qó bà oii😋😍
2025-07-08 04:17:03
2
mduyn1705
ʚ𝓂𝒹𝓊𝓎𝓃ッ🍭 :
movie năm nay có ảnh không á mn
2025-07-12 05:41:12
2
_iw.tnii
៵.𝒏𝒊𝒕˚ ༘ :
tình cũ ko rủ cx tới
2025-07-07 10:03:51
22
gianglamtruyen08
Clover x Truyện :
có code kh đka
2025-07-10 03:46:43
7
_ngtinhhmuaathuu_
❤️‍🩹 :
ảnh kid lấy đau v ạa?
2025-07-10 05:32:01
2
linh.tran.2505
Linh Trần :
xin mẫu ạ 😳
2025-07-15 13:50:03
1
thy_.trqng_
Tng :
xịn vỡi
2025-07-07 10:10:22
1
_tm.chung.o_o
Temin lowkey.✨🩸 :
vid này am hết hả bà
2025-07-09 08:18:26
5
_ntpn211
muadongparis :
edit xin v bàa
2025-07-08 01:32:07
5
cooldt.99
cool :
sao cái đoạn kia mất tiếng z
2025-07-07 10:04:31
1
yukiko.simp.senku
Kudou Yukiko :
vcl shop edit xịn thế 😭
2025-07-07 10:43:45
2
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Here are 25 core mathematical concepts that form the foundation of every ML/AI project, explained simply: 𝟭 ⤷ Mean (μ) The average of a set of values. Measures central tendency. 𝟮 ⤷ Median The middle value when data is sorted. Useful for skewed distributions. 𝟯 ⤷ Mode The value that appears most frequently in a dataset. 𝟰 ⤷ Variance (σ²) Measures how spread out the values are from the mean. 𝟱 ⤷ Standard Deviation (σ) The square root of variance shows typical deviation from the mean. 𝟲 ⤷ Skewness Measures asymmetry of the distribution. Right skew = long tail on the right. 𝟳 ⤷ Kurtosis Measures peakedness of a distribution. High kurtosis = heavy tails. 𝟴 ⤷ Probability Likelihood of an event occurring, between 0 and 1. 𝟵 ⤷ Conditional Probability P(A|B): Probability of A given B has occurred. 𝟭𝟬 ⤷ Bayes’ Theorem P(A|B) = [P(B|A) × P(A)] / P(B). Key to probabilistic inference in ML. 𝟭𝟭 ⤷ Random Variable A variable whose values are outcomes of a random process. 𝟭𝟮 ⤷ Distribution Describes how values of a random variable are spread. (e.g. Normal, Binomial) 𝟭𝟯 ⤷ Expectation (E[X]) The average value a random variable would take over many trials. 𝟭𝟰 ⤷ Covariance Measures how two variables change together. 𝟭𝟱 ⤷ Correlation Normalized covariance. Range: [-1, 1]. 1 = perfect positive, -1 = perfect negative. 𝟭𝟲 ⤷ Entropy Measure of uncertainty in information theory. Higher entropy = more unpredictable. 𝟭𝟯 ⤷ Gradient Vector of partial derivatives. Shows direction of steepest increase. 𝟭𝟴 ⤷ Loss Function Quantifies how wrong a model’s prediction is (e.g. MSE, Cross-Entropy). 𝟭𝟵 ⤷ Cost Function The average loss over the entire dataset. 𝟮𝟬 ⤷ Optimization Process of minimizing cost using algorithms like Gradient Descent. 𝟮𝟭 ⤷ Overfitting Model performs well on training data but fails on new data. 𝟮𝟮 ⤷ Underfitting Model is too simple and fails to learn patterns. 𝟮𝟯 ⤷ Bias Error from wrong assumptions in the learning algorithm. 𝟮𝟰 ⤷ Variance Error from model sensitivity to training data fluctuations. 𝟮𝟱 ⤷ Regularization Adds penalty to the loss function to reduce overfitting (L1, L2). — 𝐓𝐋;𝐃𝐑: ⤷ You can’t build solid AI/ML projects without core mathematical intuition ⤷ Learn these 25 concepts to speak the language of data #datascience
Here are 25 core mathematical concepts that form the foundation of every ML/AI project, explained simply: 𝟭 ⤷ Mean (μ) The average of a set of values. Measures central tendency. 𝟮 ⤷ Median The middle value when data is sorted. Useful for skewed distributions. 𝟯 ⤷ Mode The value that appears most frequently in a dataset. 𝟰 ⤷ Variance (σ²) Measures how spread out the values are from the mean. 𝟱 ⤷ Standard Deviation (σ) The square root of variance shows typical deviation from the mean. 𝟲 ⤷ Skewness Measures asymmetry of the distribution. Right skew = long tail on the right. 𝟳 ⤷ Kurtosis Measures peakedness of a distribution. High kurtosis = heavy tails. 𝟴 ⤷ Probability Likelihood of an event occurring, between 0 and 1. 𝟵 ⤷ Conditional Probability P(A|B): Probability of A given B has occurred. 𝟭𝟬 ⤷ Bayes’ Theorem P(A|B) = [P(B|A) × P(A)] / P(B). Key to probabilistic inference in ML. 𝟭𝟭 ⤷ Random Variable A variable whose values are outcomes of a random process. 𝟭𝟮 ⤷ Distribution Describes how values of a random variable are spread. (e.g. Normal, Binomial) 𝟭𝟯 ⤷ Expectation (E[X]) The average value a random variable would take over many trials. 𝟭𝟰 ⤷ Covariance Measures how two variables change together. 𝟭𝟱 ⤷ Correlation Normalized covariance. Range: [-1, 1]. 1 = perfect positive, -1 = perfect negative. 𝟭𝟲 ⤷ Entropy Measure of uncertainty in information theory. Higher entropy = more unpredictable. 𝟭𝟯 ⤷ Gradient Vector of partial derivatives. Shows direction of steepest increase. 𝟭𝟴 ⤷ Loss Function Quantifies how wrong a model’s prediction is (e.g. MSE, Cross-Entropy). 𝟭𝟵 ⤷ Cost Function The average loss over the entire dataset. 𝟮𝟬 ⤷ Optimization Process of minimizing cost using algorithms like Gradient Descent. 𝟮𝟭 ⤷ Overfitting Model performs well on training data but fails on new data. 𝟮𝟮 ⤷ Underfitting Model is too simple and fails to learn patterns. 𝟮𝟯 ⤷ Bias Error from wrong assumptions in the learning algorithm. 𝟮𝟰 ⤷ Variance Error from model sensitivity to training data fluctuations. 𝟮𝟱 ⤷ Regularization Adds penalty to the loss function to reduce overfitting (L1, L2). — 𝐓𝐋;𝐃𝐑: ⤷ You can’t build solid AI/ML projects without core mathematical intuition ⤷ Learn these 25 concepts to speak the language of data #datascience

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