@soymanuelq: Cuando le pides ayuda a tu amigo para hacer el portal del Nether en Minecraft #Minecraft #videosvirales #minecraftmemes #humor #fyp #minecrafthacks #minecraftmods

Soy Manuel
Soy Manuel
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Region: MX
Friday 25 July 2025 20:00:00 GMT
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nataly10331
. :
a todos nos encanta la voz 😫🫦🫦💋
2025-07-25 21:43:07
1301
alasar.reyes
Alasar Reyes :
quien me pasa el mod 🥹 el link o nombre
2025-07-26 15:46:50
7
sofia68i1
💜sofi olivie💜 :
cookie tiene su voz🫦
2025-07-25 22:40:11
750
el.follavigitas30
el follavigitas3000 :
ya vieron la personita que esta vailando en su inventario
2025-07-27 16:02:42
38
edinsonxz_
Edinson Suarez :
Quien pa jugar rlcratf en bedrock 1.21.94
2025-07-25 22:22:48
7
heven_darwin
☭Haven☭ :
yo viendo que tiene tres espadas 120 picos 69 de hierro y un tótem de la inmortalidad bailando en su inventario
2025-07-26 00:36:09
77
mr.fabby1
MR.FABBY🥷🏻 :
Día 1 INTENTANDO QUE SOY MANUEL ME SALUDE❤️👌🏻
2025-07-25 20:09:02
69
piramix102
piramix :
creo que ya se cómo lo iso con el mos de que desde el portal se vea asia afuera y uso los bloques de portal 🤓
2025-07-28 22:25:42
0
victor.taranda
Viki y Nico💝🥳 :
La vos de Cookie 😍🫦
2025-07-26 09:37:01
6
hr_zendo3
~ĤR ZƏŊƊỌ~ :
primero
2025-07-25 20:02:04
2
olivia.22.com
olivia :
si a todos nos encanta su voz del otro 😏🫦
2025-07-28 17:00:06
1
liz633771
Lix_BAKUGA :
sorprenden ustedes dos quede=🤯🤯
2025-07-25 20:10:42
22
valeriabetsabemar6
🍒valeria🍒 :
primeraaa
2025-07-25 20:09:48
3
jazminnnn.si
la tocina :
3 erroras tiene 69 de piedra 3 espadas en el mismo lugar y el totem bailando 😆
2025-07-27 20:01:15
2
sailynicolelope2412
NICOLE LÓPEZ 🧸🧸 :
ok pero la voz 🫦
2025-07-26 04:13:45
45
honeyboo8721
@honey-boo~ :
OK pero xq nadie habla del ítem del monito amarillo😭😭
2025-07-28 18:52:17
0
arturo26683
arturo :
primero en darle corason
2025-07-25 20:06:56
1
craf324
el rey :
primerooooo
2025-07-25 20:01:41
6
luisxx1._
Luis. :
Pov: le cierra la puerta y quita todo
2025-07-26 21:15:33
9
gael.olea92
gael⚽🪄🤷‍♀️ :
primero
2025-07-25 20:01:49
1
alan_lalin1
Alan_lalin1 :
y el portal del nether que es una puerta
2025-07-28 17:52:19
1
elmanantial15
ELIAS GARCIA 😎💯⚽🎺 :
el primero en dar like
2025-07-25 20:02:18
2
josh.alex328
Josh Alex :
todos: la voz de cookie yo: 3 espadas 120 picos 69 de hierro y el tótem bailando
2025-07-27 21:33:54
2
eilynsofi5
E.🌷 :
pero la voz 🫦🫦🫦
2025-07-28 13:28:00
0
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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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