@carlowrush: DO U REMEMBER!?🕺 #dance #mj #fyp

carlowrush

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Thursday 04 June 2026 23:46:54 GMT

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Housewinemami :

I THINK IM REMEMBERING 💃💃

2026-06-05 03:42:27

3509

yᥙᴛᥲ᧐Ᏽ𐌏ATsᥙ :

bro being so smooth might be criminal

2026-06-05 06:13:16

1412

youco_white :

If only MJ could see this🔥🔥🔥

2026-06-05 00:06:41

164

Myra Taylor :

Moonwalking on grass is crazy

2026-06-06 02:07:44

114

Nostradamus :

Michael wishes

2026-06-06 02:11:22

0

Tina Wms :

Dude you ate that

2026-06-05 21:21:05

19

Tay :

I wish Michael could see this cuz I know he would be smiling 🔥🔥🔥

2026-06-05 18:40:54

26

CARE FREE :

In the grass is craaazy! You ate 🔥🔥🔥🔥

2026-06-06 00:12:12

29

CaroColalillo :

ATENTOS: Michel Jackson cumple el 25 de junio cumple 17 años de muerto, y si todos nos ponemos de acuerdo para escuchar todas sus canciones ese día en Spotify y YouTube? Y rompemos otro récord a su nombre ese dia? (si te gusta la idea, escribela en otros videos en español, inglés o cualquier idioma!)

2026-06-05 17:54:55

203

hermannlili :

This is insane

2026-06-05 13:45:35

44

Jean Baptiste :

Je suis certain que Michael l’aurait adoré…

2026-06-05 09:24:04

39

Logan Roi :

Michael estaría orgulloso

2026-06-05 10:42:24

14

Lansvet :

Красавчик 👍🏻👍🏻👍🏻🎶😊

2026-06-06 10:11:28

8

Juan Pablo :

Que perro te quedo hermano te doiy un 100 de 10 😈💯 🔥

2026-06-06 01:32:11

9

Laura :

MICHAEL TE HUBIESE ELEGIDO COMO BAILARIN PARA SU GIRA. BAILAS INCREIBLE💪💪💪💪💪

2026-06-05 22:08:51

43

A.LG :

Facilmente seria um dançarino do MJ se ele estivesse vivo

2026-06-05 02:44:41

15

maju :

um dia chego nesse nível

2026-06-05 16:13:27

5

MAR C :

25 de junio cumple 17 años de muerto, y si todos nos ponemos de acuerdo para escuchar todas sus canciones ese día en Spotify y YouTube? Y rompemos otro récord a su nombre ese dia? (si te gusta la idea, escribela en otros videos en español, inglés o cualquier idioma!)

2026-06-05 22:51:01

7

Schelle :

sofort ❤️Like... einfach weil es mal anders ist und on Point zum Beat

2026-06-05 13:28:57

10

DouglasFCV :

the dance is really good, but the outfit is not appropriate

2026-06-05 17:12:01

7

leahdlovesdance :

I’m a dancer/choreographer and your moves are absolutely phenomenal! ♥️

2026-06-05 21:51:03

16

diosmioundivel :

el mejor tiktok que he visto con una canción del rey MJ 🔥🔥💯

2026-06-05 15:59:01

13

Angelina Jacqueray :

Trop fort et trop bien fait un plaisir à regarder 👌

2026-06-05 15:14:36

13

To see more videos from user @carlowrush, please go to the Tikwm homepage.

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Backpropagation is the algorithm that tells a neural network how each weight and bias contributed to its mistakes. 🧠 Here is exactly how it works: 1️⃣ Make a Prediction: The network processes the inputs using its current weights and biases. 2️⃣ Calculate Loss: A loss function measures how far the prediction is from the correct answer. 3️⃣ Propagate the Error: Starting from the output layer, the error flows backward through the network, computing how much each weight and bias contributed to the loss. 4️⃣ Compute the Gradient: This produces one derivative for every parameter, showing which direction reduces the loss the fastest. 5️⃣ Update the Parameters: Gradient descent nudges every weight and bias a small step in the direction that reduces the loss. 6️⃣ Repeat: The network repeats steps 1 through 5 over and over until its predictions become increasingly accurate. 📈 A few key things to know: ✅ Backpropagation computes the gradients—it does not update the weights itself. ✅ Gradient descent uses those gradients to actually update the weights and biases. ✅ The chain rule makes it possible to efficiently compute gradients for every parameter, even in very deep neural networks. Why it matters: ⚠️ Without backpropagation, modern neural networks wouldn't be able to learn from data. ⚠️ It makes training networks with thousands—or even millions—of parameters computationally feasible. #NeuralNetworks #Backpropagation #DeepLearning #AIEducation #MachineLearning

Backpropagation is the algorithm that tells a neural network how each weight and bias contributed to its mistakes. 🧠 Here is exactly how it works: 1️⃣ Make a Prediction: The network processes the inputs using its current weights and biases. 2️⃣ Calculate Loss: A loss function measures how far the prediction is from the correct answer. 3️⃣ Propagate the Error: Starting from the output layer, the error flows backward through the network, computing how much each weight and bias contributed to the loss. 4️⃣ Compute the Gradient: This produces one derivative for every parameter, showing which direction reduces the loss the fastest. 5️⃣ Update the Parameters: Gradient descent nudges every weight and bias a small step in the direction that reduces the loss. 6️⃣ Repeat: The network repeats steps 1 through 5 over and over until its predictions become increasingly accurate. 📈 A few key things to know: ✅ Backpropagation computes the gradients—it does not update the weights itself. ✅ Gradient descent uses those gradients to actually update the weights and biases. ✅ The chain rule makes it possible to efficiently compute gradients for every parameter, even in very deep neural networks. Why it matters: ⚠️ Without backpropagation, modern neural networks wouldn't be able to learn from data. ⚠️ It makes training networks with thousands—or even millions—of parameters computationally feasible. #NeuralNetworks #Backpropagation #DeepLearning #AIEducation #MachineLearning

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