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@tiji_f: Free Fire Edit 🔥 . . . #تيجي_فري_فاير #freefire_lover #الشعب_الصيني_ماله_حل😂😂 #creatorsearchinsights #firefreecore #bestsensitivityinfreefire #newedit #garinafreefire #freestyle #فري_فاير #free #funnymemes #foryoupage #fy #fyp #freefire #viral #freefire_meme #ff #edit #goodeditideas #freefire#freefirelover #fypfreefire #freefireedit #edit #phonkedit #firefreecore #nuevoeventofreefire #creatorsearchinsights #capcuteditforpictures #bestsensitivityinfreefire #ashtrivinovsbri #creatorsearchinsights #makingedits #editedits #newedit

🥀 𝑻𝒊𝑱𝒊 🥀
🥀 𝑻𝒊𝑱𝒊 🥀
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Wednesday 04 November 2026 11:30:00 GMT
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nazimff02
NazimFF :
محتواك قديم خير
2026-11-04 17:06:24
2
kaio01646
KAIO/كايو❤️‍🔥⚡ :
اعمل فيديو لتصميم مش للجودة
2026-11-05 11:32:20
1
segara_300g
SG ꜱɪɢᴀʀᴀ ⸙ :
نزل شرح للجوده
2026-11-04 13:21:37
2
k_ajool
yahia_mh :
2026-11-05 14:06:20
2
vinoxgamerr
𝑽𝒊𝒏𝒐𝒙 🥷 :
l7ob kamel🥰❤️
2026-11-04 11:48:28
1
spider_h7
معتزل :
علمني
2026-11-04 17:06:42
1
mohammed.hany4384
Mohammed Hany 👑 :
2026-11-04 13:51:19
1
segara_300g
SG ꜱɪɢᴀʀᴀ ⸙ :
2026-11-04 13:21:15
1
zinoff244
ZINO ❷ :
ديرلي جام على فيديواتي انا عاملك جام على كل فيديواتك أرجوك
2026-11-05 19:09:31
1
chahin20147
ＣＨA💫ＨＩＮ!! :
❤️❤️❤️❤️❤️❤️❤️❤️❤️
2026-11-04 11:31:24
1
user5730817520108
™SAHMD 10k :
[قلب أحمر][قلب أحمر][قلب أحمر][قلب أحمر]
2026-11-04 11:31:56
0
ugc.cikiko
UGC SIKIKO :
[قوة][قوة][قوة]
2026-11-06 13:43:24
1
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Other Videos

Replying to @skyyla2639 update I got an answer from someone else. only sharing reality with you guys because I get thousands of comments and DM’s a day that I have people’s dream life and they are so jealous and while I am so so so grateful to have found a new way to get paid to travel i am also human and i exist and my feelings matter and rn everything sucks and i dont feel that inspiring rn 😂😂🤧🖤💔
Replying to @skyyla2639 update I got an answer from someone else. only sharing reality with you guys because I get thousands of comments and DM’s a day that I have people’s dream life and they are so jealous and while I am so so so grateful to have found a new way to get paid to travel i am also human and i exist and my feelings matter and rn everything sucks and i dont feel that inspiring rn 😂😂🤧🖤💔


➡️ Machine Learning from Scratch, part 15: a recommender, the kind of algorithm that picks what your feed shows you. Our app is simulated: 300 people and 60 videos. Every person has a hidden taste, every video has a hidden style, and each person has watched only about 12 of the 60 videos. All the algorithm ever sees is who liked which of the videos they watched. How it learns: every person gets two numbers, and every video gets two numbers. Think of them as arrows on a map. For every video a person has seen, the two arrows turn into a chance of a like with the sigmoid from part 2. The algorithm compares that chance with what really happened and nudges both arrows a little. That is gradient descent from part 8, and the whole method is called matrix factorisation. The results, all measured on the videos nobody showed it: After 25 rounds it guesses 85.0% of the hidden likes right. After 3000 rounds it gets 87.8% right. The best any method could do here is 91.1%, because some likes are just random. For every person it recommends the three unseen videos with the strongest match, and 98% of them are videos the person really likes. A random unseen video gets a like 51% of the time. The first person on the list watched only 5 videos. From those five it guessed the other 55 and got 51 of them right. We ran the same program on 20 different simulated apps, and it always landed between 84 and 88%. Real apps use hundreds of numbers for every person and every video, plus signals like watch time and skips. The core idea stays the same: learn numbers for people and for videos, and match them. #machinelearning #ai #recommendersystem #python #coding
➡️ Machine Learning from Scratch, part 15: a recommender, the kind of algorithm that picks what your feed shows you. Our app is simulated: 300 people and 60 videos. Every person has a hidden taste, every video has a hidden style, and each person has watched only about 12 of the 60 videos. All the algorithm ever sees is who liked which of the videos they watched. How it learns: every person gets two numbers, and every video gets two numbers. Think of them as arrows on a map. For every video a person has seen, the two arrows turn into a chance of a like with the sigmoid from part 2. The algorithm compares that chance with what really happened and nudges both arrows a little. That is gradient descent from part 8, and the whole method is called matrix factorisation. The results, all measured on the videos nobody showed it: After 25 rounds it guesses 85.0% of the hidden likes right. After 3000 rounds it gets 87.8% right. The best any method could do here is 91.1%, because some likes are just random. For every person it recommends the three unseen videos with the strongest match, and 98% of them are videos the person really likes. A random unseen video gets a like 51% of the time. The first person on the list watched only 5 videos. From those five it guessed the other 55 and got 51 of them right. We ran the same program on 20 different simulated apps, and it always landed between 84 and 88%. Real apps use hundreds of numbers for every person and every video, plus signals like watch time and skips. The core idea stays the same: learn numbers for people and for videos, and match them. #machinelearning #ai #recommendersystem #python #coding
#ffesportsid #FFWSGF2026 #FreeFire
#ffesportsid #FFWSGF2026 #FreeFire
Las reflexiones del Pitbuull
Las reflexiones del Pitbuull
ตอบกลับ @🔥🔱⚡Lucifer⚡🔱🔥 กันน้ำได้คับ ^^ เป็นวัสดุใช้ภายนอกอยู่แล้วจ้า #T3B #tiktokuni #รู้จากtiktok #DIY #tiktokดีไอวาย #ต่อเติมบ้าน #บ้านน๊อคดาวน์ #ไฟเบอร์ซีเมนต์
ตอบกลับ @🔥🔱⚡Lucifer⚡🔱🔥 กันน้ำได้คับ ^^ เป็นวัสดุใช้ภายนอกอยู่แล้วจ้า #T3B #tiktokuni #รู้จากtiktok #DIY #tiktokดีไอวาย #ต่อเติมบ้าน #บ้านน๊อคดาวน์ #ไฟเบอร์ซีเมนต์

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