@8555plokv:

مكيائيل
مكيائيل
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Thursday 23 July 2026 11:58:58 GMT
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zain_alabideen0
Mr. Zain🇸🇩/مستر زين :
انت لابس رمز الدولة مفروض تعكس حاجه كويسة للمشاهد
2026-07-24 10:31:22
56
user7963186654356
بقاريه ورده البقاره :
رقم الإختلاف السياسي مستمتعه من اللابس الكاكي يا رب يكون من نصيبي😂😂✌️
2026-07-24 10:01:13
8
user5522399801229
عاشقه الصمت :
اثنين في واحد بي مزاج
2026-07-23 20:00:19
3
krim15147
Abou Malak :
انت رجل الدولة كدا اهااا نحنا نعمل شنو دا شي ما بقدمنا
2026-07-24 13:38:08
2
user1668663369384
البيشي أحمد البيشي :
ريك شنو✈️✈️
2026-07-25 14:48:06
0
mezorryh
🏴‍☠️ميزو تشويش| Mezo🥷 :
الله يكتلني ابليس زاتو مستمتع منك🫡🎤💸
2026-07-23 21:45:30
2
user796869233247
QUSAY :
وارد في الجيش
2026-07-24 20:35:09
1
alo17sh
🇸🇩_Ali 👑 :
احترم رمز الدوله الانت لابسو دا انت مفروض تعكس حاجه كويسه تحياتنا ليك 🖐
2026-09-06 18:05:40
2
user71713423834561
ام كنان وكيان :
اساطير كبار لكن اتلومت فى الكبك
2026-07-24 08:36:56
4
hano1727
حنية 0024 :
وبعدين قولوا ظواهر سالبه
2026-07-24 12:25:33
2
user98781594404523
لوفي :
فى حاجه اسما استراحة محارب عشان يطير من جو الحرب
2026-07-26 10:05:35
0
.wade.amor
وادي أمور WaDe Amor :
من ألمفترض تتحاسب أمام الدولة لابس رسمي وتعمل كدا و مواطن يعمل كيف 😂
2026-07-27 16:51:23
3
user94403730708338
برعي الكبشاب :
شارع الدولة يا بشر
2026-07-24 11:27:12
3
user2254399423592
علمتني الحياة :
مستمتع. منكم يا مكنات🥰🥰🥰
2026-08-06 11:39:06
1
zaherkmaal
عاشق ريري ❤️ :
والله السفة دي زكرتني كيسي😂
2026-07-27 15:56:05
1
user8155938670822
موسي الجني :
رساله
2026-07-29 13:48:25
1
mohanadaymam0
👻🗯هنو تويكس❤‍🩹💜❤️‍🔥❤️‍🩹 :
احترم الكاكي ده ي مكتب بس♥️♥️
2026-07-24 09:14:18
1
user2676132461880
ام ملاك :
ملوك الكوميد مكنات بمزاج
2026-07-28 10:16:43
1
user8703352819141
مجنونه جن :
يمثل زوجي 😁😁
2026-07-24 09:37:02
1
.2494286
الليث DH🇸🇩🇾🇪🦅🫡 :
حكومة والموطن مظلام✌️
2026-07-25 09:36:37
1
mahammadhafiz72
دولي ود الدولة العثمانية 🤑🥰 :
ناس الكوميد تحياتي يا مكنات الخريف قرب😃
2026-07-24 08:20:33
1
user9926375346116
الصادق الصادق :
عبادب😎😎
2026-07-25 11:58:56
0
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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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