@sisirkhan055: যাও আজকের পর আর তোমাকে বিরক্ত করবো না,, তোমাকে পাওয়া কোন আশা আমি করবো না,,ভালো থাকো তুমি 😭😭

꧁💔SISIR💔꧂

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Saturday 03 October 2026 13:24:03 GMT

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👑জুনাইদা 👑 :

অনেক ভালো লাগে আপু আপনার ভিডিও

2026-10-03 14:13:57

3

MD Ataur :

পাখির ডানায় ভর করে আসা ভোরের প্রথম আলোটা মনে করিয়ে দেয় যে প্রতিটি দিনই এক নতুন সূচনার বার্তা বয়ে আনে 🖤

2026-10-04 07:08:20

0

user2958834087584 :

🥰🥰🥰🥰 তাই নাকি আপু

2026-10-04 07:25:37

0

তুমি আমার মনের মানুষ SA :

তাই নাকি

2026-10-04 07:46:25

0

🌺🌺 জীবন্ত লাশ আমি সাথী ❣️❣️ :

হুম রাইট গো কলিজার আপু সাপোর্ট

2026-10-04 04:35:52

0

shafiul Islam shafiul :

❤️🎋:মাশাল্লাহ মাশাল্লাহ খুবই সুন্দর একটি ভিডিও পরান জুড়ানোর মতন একটি ভিডিও তার থেকে ভালো লাগে তোমাকে বন্ধু তুমি অনেক সুন্দর সুন্দর সুন্দর ভিডিও বিনোদন দাও😳😳

2026-10-03 18:00:57

0

আমিনুর ইসলাম :

2026-10-04 07:15:52

0

সাপোট🥀 চাই👍একটু 🥀নীলফামারী :

hi

2026-10-03 13:27:12

1

ছোটো ইউজার শাওন আহাম্মেদ :

ভালবাসার অবিরাম

2026-10-04 04:07:33

0

Ã š Î f v å Ï❤️{S} :

কি কষ্ট

2026-10-03 16:13:06

0

SUJON😍1234 :

ভালোবাসা না এতো কষ্ট 😭আর🖤

2026-10-03 15:57:37

0

Md Limon :

❤️❤️❤️🥰🥰🥰

2026-10-03 15:26:17

0

আমিনুর ইসলাম :

2026-10-04 07:15:48

0

01733695526 :

🥰🥰🥰

2026-10-03 13:45:06

1

khokonislam1232 :

👍👍👍

2026-10-04 11:04:32

0

md Miraj islam :

❤️❤️❤️

2026-10-03 13:25:52

1

SM Apon :

🥀🥀🥀

2026-10-04 10:54:30

0

Yaseen On Fair 1 2 3 :

😱😱😱😱

2026-10-03 13:27:35

1

আকাশ ছোয়া ভালোবাসা :

💖💖💖

2026-10-04 10:42:10

0

user089268391আলী আহসান :

[Gesture OK][Gesture OK][Gesture OK]

2026-10-03 13:50:53

1

Sk Sahon :

❤️❤️❤️

2026-10-04 10:39:00

0

❤️❤️Probasi koliza❤️❤️🇸🇦🇸🇦 :

🥰🥰🥰

2026-10-03 13:56:58

1

user6263363781377 :

💓💓💓

2026-10-04 10:27:57

0

Jotun Datta :

❤️❤️❤️

2026-10-03 13:52:27

1

মনির আপনার ভালোবাসা পেতে চাই :

❤️❤️❤️

2026-10-03 13:28:17

1

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➡️ Machine Learning from Scratch, part 24: k-nearest neighbors algorithm Our photos are simulated. A small program of ours placed 900 photos from three made-up trips on the same kind of map as in part 17: a city trip, a week on the coast and a mountain hike. 600 come with their trip (250 city, 200 coast, 150 mountains), and 300 more are held back for the test. A few were taken on the drives, and some of those sit among another trip's photos. How it works: A new photo comes in. The program measures its distance to all 600 labeled photos, keeps the k closest and lets them vote with their trips. The most votes win. There is no training step, the program simply keeps every labeled photo. In part 17, k-means had to find the trips without any labels; k-nearest neighbors uses the labels it already has. Our example is a new city photo. Its two closest photos are coast photos snapped on the drive through the city (0.91 and 1.20 km away), the next three are city photos. With k = 5 the vote is 3 city to 2 coast, which is right. With k = 1 it copies the single closest photo and says coast, which is wrong. The test on the 300 held-back photos: k = 1: 285 of 300 right k = 5: 295 of 300 right In all 12 photos that k = 1 gets wrong and k = 5 gets right, the closest photo was an odd one from a drive. The 5 that k = 5 still misses were all taken on a drive, right among another trip's photos. Why it matters: It needs no training and works on any data where similar things sit close together. It is one of the top 10 algorithms in data mining, on the same list as k-means from part 17. Sources: Wu et al.,

➡️ Machine Learning from Scratch, part 24: k-nearest neighbors algorithm Our photos are simulated. A small program of ours placed 900 photos from three made-up trips on the same kind of map as in part 17: a city trip, a week on the coast and a mountain hike. 600 come with their trip (250 city, 200 coast, 150 mountains), and 300 more are held back for the test. A few were taken on the drives, and some of those sit among another trip's photos. How it works: A new photo comes in. The program measures its distance to all 600 labeled photos, keeps the k closest and lets them vote with their trips. The most votes win. There is no training step, the program simply keeps every labeled photo. In part 17, k-means had to find the trips without any labels; k-nearest neighbors uses the labels it already has. Our example is a new city photo. Its two closest photos are coast photos snapped on the drive through the city (0.91 and 1.20 km away), the next three are city photos. With k = 5 the vote is 3 city to 2 coast, which is right. With k = 1 it copies the single closest photo and says coast, which is wrong. The test on the 300 held-back photos: k = 1: 285 of 300 right k = 5: 295 of 300 right In all 12 photos that k = 1 gets wrong and k = 5 gets right, the closest photo was an odd one from a drive. The 5 that k = 5 still misses were all taken on a drive, right among another trip's photos. Why it matters: It needs no training and works on any data where similar things sit close together. It is one of the top 10 algorithms in data mining, on the same list as k-means from part 17. Sources: Wu et al., "Top 10 algorithms in data mining" (2008) #machinelearning #ai #coding #python #numpy

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Năm 17 tuổi đâu biết năm 27 tuổi t mình sẽ như thế nào

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#لانا_بيوتي #اسيل_عمران @Lojain

#لانا_بيوتي #اسيل_عمران @Lojain

#BAYESAM1998

Khăn rửa chén bằng dây kim loại #nhoreview #khanruabatdaythep

Khăn rửa chén bằng dây kim loại #nhoreview #khanruabatdaythep

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