@_designer93: @محمد الحربي #اكسبلورexplore #الشعب_الصيني_ماله_حل😂😂 #شباب_البومب #fyp #محمد_الحربي

مصمم || s

Open In TikTok:

Region: SA

Monday 01 June 2026 20:53:01 GMT

3761588

80578

1387

4990

Music

Download

No Watermark .mp4 (3.06MB) No Watermark(HD) .mp4 (3.06MB) Watermark .mp4 (0MB) Music .mp3

Comments

... :

كيف عرفت اني استخدم افوجين

2026-06-02 16:08:34

557

Viver647 :

بيتزوج لين البارقي😂😂😂😂😂

2026-07-31 05:27:49

5

🦌cR7 :

هو نفسه ذا

2026-08-21 04:36:39

109

m... :

أنا ١٦ اكبر منه وما طلعت لي لحيه

2026-06-02 09:47:24

2262

مشاري العتيبي🇸🇦 :

اخذ خلطة عصومي وين حصلها

2026-06-02 10:15:29

2261

『𓆩الحـارثــي𓆪』 :

كم عمره

2026-06-02 03:14:41

22

صويلح محمد الرشيدي :

سبحان الله

2026-06-03 10:57:46

296

شيخة |shekha :

ابني عمره 19وماعنده لحيه

2026-07-25 15:24:41

25

! :

ترا معاه فلوس ماشالله تبارك الله

2026-06-04 17:58:20

9

⚜️SULTAN🇬🇧 :

: العالم تكبر بسرعه ولا الزمن معلق عندي 😂😂😂😂

2026-06-02 09:28:52

291

A :

ترا مواليد2009 يعني عمره17 او18

2026-06-02 15:16:09

46

707 :

لحضه لحضه هو قال قريب

2026-06-17 18:35:47

9

[معاالله نحيا}{🇾🇪}𓆩🦅 :

أحنا بتعزمنا

2026-06-03 14:43:09

11

77L :

والله اني كنت اكبر منه

2026-06-12 20:00:24

7

زهرا بنت اصول😎🔥 :

فديت قلبك تجنن

2026-06-06 22:04:46

7

𝓔𝓿𝓪𝓷 𝓚𝓲𝓷𝓰 👑 :

عمري 21 ولسى بدون لحيه💔🙂

2026-06-03 11:50:51

15

مـحـــمــد :

مين؟ شاف الفيديو الساعه 8:00

2026-06-13 04:51:02

15

٥٣ # 🌶️ :

هذا متى صار ريال

2026-06-14 14:10:21

7

L7 :

انا عمري ٢١ سنه واشوف نفسي توي صغير وهو يبي يتزوج😂😂😂

2026-06-09 17:34:32

9

Mohammed :

أكثر انسان اشوفه يكبر بسرعه هادي ماشاء لله

2026-06-02 21:02:52

9

✨🧸🤎 :

فدوه

2026-06-01 21:23:41

17

Falah :

كبر وانا ما كبرت ماشاءالله

2026-06-02 12:49:03

7

M’ :

زمان كنت اكبر منه واليوم هو اكبر مني ؟؟؟

2026-06-02 16:06:02

8

ثامـر | Thamer :

هذا كيف يكبر بدقيقه. ماشاءالله تبارك الله

2026-06-02 03:56:33

12

To see more videos from user @_designer93, please go to the Tikwm homepage.

Other Videos

akhir pekan. #spongebobsquarepants #patrick #cartoon #katakata

akhir pekan. #spongebobsquarepants #patrick #cartoon #katakata

aqqq meubemm @nique || #meme #trending #nayeon #fypシ #forupage

aqqq meubemm @nique || #meme #trending #nayeon #fypシ #forupage

Clean look, effortless comfort. 🤎 Kemeja longsleeve Collarless Airflow Breezetech hadir dengan desain kerah minimalis dan material yang ringan, adem. #Maisonwells #AirflowBreezetech #racuntiktok #fyppppppppppppppppppppppp

Clean look, effortless comfort. 🤎 Kemeja longsleeve Collarless Airflow Breezetech hadir dengan desain kerah minimalis dan material yang ringan, adem. #Maisonwells #AirflowBreezetech #racuntiktok #fyppppppppppppppppppppppp

Five machine learning algorithms. One graph. That's the whole trick. Most explainers give each algorithm its own diagram, so you never actually see how they differ. This one puts all five on the SAME pair of axes — and the difference becomes the shape each one draws on it. 1 — LINEAR REGRESSION → draws a LINE You run a delivery company. Distance in, minutes out. It finds the line with the smallest total error, then predicts a number. Real fit here: time = 7.6 + 2.52 × distance. A 12 km job → 37.8 minutes. 2 — LOGISTIC REGRESSION → draws an S-CURVE Despite the name, it classifies. Will this subscriber cancel? The output isn't $37, it's a probability. 17 days since last login → 82% chance of churning. Cross your threshold and that user gets flagged high risk. 3 — DECISION TREES → draws RECTANGLES Hip-hop? Yes. Energetic? Yes. Late at night? No. → Workout playlist. Here's the part nobody shows you: those three questions are literally cuts in the plane. The tree and the carved-up graph are the same object. 4 — SVM → draws a GAP Two species of iris. Plenty of lines separate them — SVM finds the one with the widest possible gap. That gap is the margin (1.397 cm here), and it rests on just 3 points. Add the kernel trick and the boundary can curve: a straight line gets 60%, a curve gets 100%. 5 — KNN → draws NOTHING No line, no curve, no tree. A new film lands in feature space, it checks the 5 closest, 4 are sci-fi, done. The trade-off: it never builds a model, so every prediction re-scans the whole library. 68 examples is instant. 4,200,000 is not. The one-line version: Linear predicts a number. Logistic predicts a class, with a probability. Trees decide by asking. SVM finds the widest boundary. KNN copies its closest neighbours. Every number on screen came from an actual fitted model — and the flowers are the real Fisher iris dataset, not a drawing. Which one finally clicked for you? 👇 #machinelearning #datascience #artificialintelligence #mlengineer #datascienceforbeginners

Five machine learning algorithms. One graph. That's the whole trick. Most explainers give each algorithm its own diagram, so you never actually see how they differ. This one puts all five on the SAME pair of axes — and the difference becomes the shape each one draws on it. 1 — LINEAR REGRESSION → draws a LINE You run a delivery company. Distance in, minutes out. It finds the line with the smallest total error, then predicts a number. Real fit here: time = 7.6 + 2.52 × distance. A 12 km job → 37.8 minutes. 2 — LOGISTIC REGRESSION → draws an S-CURVE Despite the name, it classifies. Will this subscriber cancel? The output isn't $37, it's a probability. 17 days since last login → 82% chance of churning. Cross your threshold and that user gets flagged high risk. 3 — DECISION TREES → draws RECTANGLES Hip-hop? Yes. Energetic? Yes. Late at night? No. → Workout playlist. Here's the part nobody shows you: those three questions are literally cuts in the plane. The tree and the carved-up graph are the same object. 4 — SVM → draws a GAP Two species of iris. Plenty of lines separate them — SVM finds the one with the widest possible gap. That gap is the margin (1.397 cm here), and it rests on just 3 points. Add the kernel trick and the boundary can curve: a straight line gets 60%, a curve gets 100%. 5 — KNN → draws NOTHING No line, no curve, no tree. A new film lands in feature space, it checks the 5 closest, 4 are sci-fi, done. The trade-off: it never builds a model, so every prediction re-scans the whole library. 68 examples is instant. 4,200,000 is not. The one-line version: Linear predicts a number. Logistic predicts a class, with a probability. Trees decide by asking. SVM finds the widest boundary. KNN copies its closest neighbours. Every number on screen came from an actual fitted model — and the flowers are the real Fisher iris dataset, not a drawing. Which one finally clicked for you? 👇 #machinelearning #datascience #artificialintelligence #mlengineer #datascienceforbeginners

Luigi Time! Cosplay #supermario #cosplay #foryoupage #costume #fyp

Luigi Time! Cosplay #supermario #cosplay #foryoupage #costume #fyp

About

Robot
API

Legal

Privacy Policy