@atajanov_22.03: Barcelona ❤️💙 #rek #foryou #barcelona

Atajanov
Atajanov
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Region: TM
Tuesday 20 October 2026 09:52:57 GMT
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batyrov_o7_
batyrov🧢 :
psg 4 :2😂
2026-10-20 23:21:26
5
meyliscik10
Meyso🤙🏻🖤 :
su gun garas gorers ollany
2026-10-20 16:01:16
3
pes_kobel
Abdy_7 :
Real utmagy arzuw edersin
2026-10-21 02:21:49
3
orazmedow_08
ᬊᬁ꯭Ꮐ꯭ᥙ꯭ꭱ꯭ᏼꪒ̵꯭ꪀ꯭ꪀ꯭ᥱ꯭ꮲᥱ꯭ຣ꯭🪖 :
Visca barca ❤️💙
2026-10-21 03:48:16
3
polat.512
𝐟𝐞𝐧𝐨𝐱 :
2-1 agyr degyami?
2026-10-26 00:50:12
0
esenovv_7o
𝗘𝘀𝗲𝗻𝗼𝘃𝘃𒌐 :
4:2
2026-10-20 21:06:29
1
user3408797979523
user3408797979523 :
😂😂😂😂😂😂
2026-10-20 10:00:32
2
dovletof_77
᪤.Ꭼ ꓒ ᥱ ꮲ ຣ ꪱꪲ ຂ ຂ ⚜️ ᜪ̵ ⧽ :
2 1 😅
2026-10-25 22:40:09
1
gyzyklanma7_
nunisx🌷 :
Barca💙❤️
2026-10-24 05:46:00
1
soyunow48
SOYUNOW__7 :
2026-10-22 05:53:36
2
pes_kobel
Abdy_7 :
Psg 4-2 Barcelona [Слезы радости]
2026-10-21 02:22:05
3
kakadurdy_edits_best
ᴋᴀᴋᴀᴅᴜʀᴅʏ.ᴇᴅɪᴛs :
4:2 psg😂
2026-10-21 07:33:33
1
kakox87
Kako_🪐 :
ufffffff
2026-10-22 04:55:22
1
sxrin.privvv
Sxrin.privvv🇪🇦 :
Visca_Barca🇪🇸
2026-10-23 02:09:46
1
id52406182832
jr :
2026-10-21 17:21:29
1
nurberdie0
Azym sheykh🇪🇪🗿 :
4 :2
2026-10-21 08:01:48
0
esto_vayner
Kiçi_Esto👑 :
visca barca
2026-10-20 14:34:55
3
dilsatbek.eminow1
dilşatbek_ :
Visca Barca 💙❤️
2026-10-25 11:19:18
1
eldarcik133
Eldarcik_tm_011 :
@Abdy_7:Psg 4-2 Barcelona [Слезы радости]
2026-10-22 13:23:38
1
dowrantt
Dowran_tt :
2026-10-22 08:21:10
1
ballyyev0
ᴰᵃʳᵏ ᴬᵐᶻ 🎧❗ :
Viska barsa 💙❤️
2026-10-23 04:18:33
1
user2704.4
𝙔𝙜𝙩𝙮ý𝙖𝙧.𝘼𝙚 :
2026-10-21 11:06:23
1
matadoraccaunt
Bilmek_gerek⚜️däl.! :
wisca barsa
2026-10-23 14:00:00
1
_arsi_7_
Arsi_7Ꞵ :
Viska barsa viska katalunya🔴🔵
2026-10-22 10:21:41
1
www.tiktok.comrelax5
Relax❗ :
wiska barsa wiska Katalonya
2026-10-20 12:47:05
1
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Other Videos

Nobody ever wrote the code that lets ChatGPT write Python. Not one line of it. 🤯 Here's how a large language model actually gets built, step by step: 1️⃣ Data. Trillions of words of books, web pages, code and forums, then cleaned: duplicates removed, spam filtered, broken text stripped. One open dataset, FineWeb, is 15 trillion tokens of web text. 2️⃣ Tokens. The model never sees words.
Nobody ever wrote the code that lets ChatGPT write Python. Not one line of it. 🤯 Here's how a large language model actually gets built, step by step: 1️⃣ Data. Trillions of words of books, web pages, code and forums, then cleaned: duplicates removed, spam filtered, broken text stripped. One open dataset, FineWeb, is 15 trillion tokens of web text. 2️⃣ Tokens. The model never sees words. "Unbelievable" becomes 4 pieces: Un · bel · iev · able. Each piece becomes a number, and each number becomes a vector of hundreds of numbers. 3️⃣ The transformer. Stacks of layers, and inside each one, attention. In "the programmer fixed the server because it crashed", a real GPT-2 attention head sends 54% of "it"'s attention straight to "server". 4️⃣ Pre-training. One task, repeated trillions of times: predict the next token. Guess wrong, measure how wrong, nudge the weights a tiny bit downhill. Meta trained Llama 3 on up to 16,000 GPUs. 5️⃣ Post-training. A pre-trained model is just a very powerful autocomplete. Fine-tuning on good examples, human feedback and checkable rewards (did the math match? did the code pass?) turns it into an assistant. 6️⃣ Serving. A 405-billion-parameter model needs about 810 GB just for its weights. One GPU holds 80. So it gets split, compressed and batched so millions of people can use it at once. Every example in the video comes from a real model: the tokens, the attention, even the loss landscape. The weird part? Writing code, explaining physics, translating French: none of it was programmed. It all emerges from one tiny objective, repeated at enormous scale: predict what comes next, and get slightly less wrong every time. Which step surprised you most? Drop the number 👇 Save this for the next time someone calls AI "just autocomplete." #llm #artificialintelligence #machinelearning #chatgpt #techexplained

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