@ta.kwa30: يوماً ماَ معَ من أحب يا الله ♥️🤲🏻🕋💍

❣️تقوى&نادر❣️
❣️تقوى&نادر❣️
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Region: TN
Wednesday 16 September 2026 18:43:24 GMT
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fathi.korchi4
Fathi Korchi :
One day, with my soulmate and the fruit of our union, God willing.
2026-09-24 13:00:59
0
user5617620800321
سندس سندوسة :
يارب
2026-09-16 20:12:37
2
user6139955635449
أم المؤنسات :
أمين يارب
2026-09-25 13:27:43
0
rj4449
🫰🏻 :
ان شاء الله ياربي اجمعين 🤲🏻🥰🫰🏻
2026-09-16 18:46:55
2
user5768144940631
user5768144940631 :
ياربي أمين
2026-09-23 21:14:36
0
fouzibouab
Fouzi Bouab :
يارب امين
2026-09-25 15:37:21
0
user606594781503
نجم الدين :
والله
2026-09-24 21:07:09
0
sihemsam1
sihemsam1 :
يا رب
2026-09-25 15:24:35
0
tma.tma742
Téma Téma :
nchalah
2026-09-24 17:54:46
0
user7825339524820
افتخار👑افتخار :
مع أمي نشالله يارب
2026-09-24 23:49:04
0
naboli2137
❤️‍🔥❤️‍🔥المهاجر❤️‍🔥❤️‍🔥 :
مع زوجتي انشاء الله
2026-09-24 22:25:31
0
mohhoho251
صهيب جيجل :
ربي يحقك لكي امنيتكي
2026-09-24 20:24:33
0
user628802284
oum hamza :
ومن انقللكم انا الله يعطني غمة الي تغمني تقلولي لالا ماتدعيش علا روحك
2026-09-24 21:25:57
0
tiktok56789064
الفتى الانيق :
ان شاء الله ربي يرزقني زوجة صالحة ونروح ان شاء الله
2026-09-24 19:29:29
0
frogdida
frogida :
يارب عودة قريبة ان شاء الله [Mains ouvertes][Mains ouvertes][Mains ouvertes]
2026-09-24 19:15:08
0
adame.mona
Adame Mona :
ان شاءالله ياربي مع من نحب
2026-09-24 12:56:57
0
user475785992015
ebn kalby💫🌝 :
انشالله وعدني يوديني عمره سوه ربي يكتبها الي وحبيبي سوه انشالله
2026-09-24 22:35:14
0
www.titok.comwwww.t5
🇹🇳🫶❤️شوقي نجوم الليل ❤️😘🫶 :
إنشاء الله ياربي 😭🤲 أمين ياربي
2026-09-16 20:20:11
0
user6187849383817
ال محفوظية :
يارب
2026-09-24 08:15:51
0
fatizara074
fatizara :
inchallah yarbi
2026-09-24 03:26:15
0
hoodo22.2
Hoodo_هــودة :
يوم ما ان شاءالله 🥹🥹❤️
2026-09-25 16:12:08
0
ghillaslyonais
Ghilas Lyon :
Amine y’a rabi
2026-09-24 12:32:02
0
sidahmedchenine
Sidou tigre :
ان شاء الله مع زوجة صالحة ربي يرزقنا الحلال والعفة
2026-09-24 07:49:35
0
sofdeutschland
sofdeutschland :
اللهم آمين يا رب
2026-09-24 10:29:42
0
norabelghiti8
ناني🇪🇦🥰🇲🇦 :
ياربي
2026-09-16 20:58:58
0
To see more videos from user @ta.kwa30, please go to the Tikwm homepage.

Other Videos

➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding
➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding

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