@ll._.mehran._.ll0: #foryou #💔🥀🥺

𓍼.مࣿــــهرانࣿ.

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Sunday 27 September 2026 06:33:46 GMT

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arsalakakar3 :

wah wah heart touching aong

2026-09-28 19:02:40

0

MD✌ KOCHAI❤ :

ما فګر کول زما د موبایل سکرین خراب سو😨😨

2026-09-28 17:36:02

0

🐊مــــــا ســـــټــــــر SK🐊 :

Washhh 🥺💔

2026-09-28 21:38:40

0

💜 𝑺𝑲 𝑻𝒚𝒑𝒊𝒔𝒕 💛 :

superb editing bro 🌹

2026-09-28 18:17:56

0

¥تنها جان€£ :

واخي رښتيا واىى بس هرڅه نمګړي ډي

2026-09-28 02:25:18

4

👑👿 PuخToon👿👑 #👑PuخToon❤️ :

hayyy 🥹

2026-09-27 19:28:42

2

🇵🇰Faisal Khan💞Awan🇦🇪 :

2026-09-27 13:32:37

4

⚔️302سرکار ⚔️ :

nice

2026-09-27 17:40:26

3

𝑯𝒐𝒓𝒂ᥫ᭡ :

2026-09-28 05:53:15

2

🤫▄︻̷̿Νουρ┻̿═━一❤️‍🔥 :

2026-09-27 18:56:20

1

👑فیصل خان👑 :

2026-09-28 18:51:01

0

🦋⃝ بــدرنګــه 🦋⃝ :

🥺🥺🥺

2026-09-27 14:47:44

2

hfeez khan :

2026-09-28 14:54:20

0

Ghafur Kharoti :

🥰🥰🥰

2026-09-28 17:59:27

0

مينه ناک afghan :

2026-09-27 17:56:58

1

🔥 𝐀𝐊_𝐊𝐇𝐚𝐍 🔥 :

2026-09-28 15:11:16

0

𓆩*𓆪𝒑𝒐𝒍𝒂𝒅 𝒌𝒉𝒐𝒔𝒕𝒊💔 :

2026-09-28 08:57:44

0

WK🥀 :

2026-09-28 09:16:24

0

😘Ahmad😘Sha😘Afghan😘 :

❤️❤️❤️❤️

2026-09-28 11:50:54

0

Asad Khan :

دنیا پانی😭😭😭

2026-09-28 16:34:38

0

𝕷𝖔𝖓𝖊 𝖂𝖔𝖑𝖋 :

Check my account

2026-09-27 14:07:13

0

♡ 𝙃𝘼𝙈𝙄𝘿𝙊༻ :

چی کله مړ شوم په دغه سندره رافسی ستوری واچوه ..!؟💔🥺

2026-09-27 06:36:26

3

✌.MR.jasim.302.✌💝 :

jarrr sham

2026-09-28 10:32:14

0

استغفرالله :

تا بیا جلا خوند په کړی دی

2026-09-28 05:25:56

0

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What if an AI model didn’t just give you a prediction — but also showed you the concepts it used to get there?

That’s the idea behind **Concept Bottleneck Models (CBMs)**.

Instead of mapping an input directly to an output, a CBM introduces an interpretable intermediate layer:

**Input → Concepts → Prediction**
**x → c → y**

In this carousel, we break down the key ideas step by step:

→ Why Concept Bottleneck Models are useful
→ How the two-stage prediction architecture works
→ What makes a “concept” different from a latent feature
→ Which model architectures can implement a CBM
→ How humans can intervene and correct concept predictions
→ How CBMs differ from post-hoc XAI methods such as SHAP
→ Where information can be lost in the bottleneck
→ Where CBMs can be applied beyond image classification

The central idea is simple but powerful:

**Interpretability is not added after the prediction — it becomes part of the prediction pathway itself.**

At the same time, interpretability comes with important design questions: Are the chosen concepts informative enough? Can they be predicted reliably? And do they capture the information needed for the downstream task?

Swipe through for a visual introduction to one of the most interesting approaches in interpretable machine learning.

Save this carousel if you’re learning **Explainable AI**, and share it with someone working on interpretable ML.

#ConceptBottleneckModels #ExplainableAI #XAI #MachineLearning #ArtificialIntelligence

What if an AI model didn’t just give you a prediction — but also showed you the concepts it used to get there? That’s the idea behind **Concept Bottleneck Models (CBMs)**. Instead of mapping an input directly to an output, a CBM introduces an interpretable intermediate layer: **Input → Concepts → Prediction** **x → c → y** In this carousel, we break down the key ideas step by step: → Why Concept Bottleneck Models are useful → How the two-stage prediction architecture works → What makes a “concept” different from a latent feature → Which model architectures can implement a CBM → How humans can intervene and correct concept predictions → How CBMs differ from post-hoc XAI methods such as SHAP → Where information can be lost in the bottleneck → Where CBMs can be applied beyond image classification The central idea is simple but powerful: **Interpretability is not added after the prediction — it becomes part of the prediction pathway itself.** At the same time, interpretability comes with important design questions: Are the chosen concepts informative enough? Can they be predicted reliably? And do they capture the information needed for the downstream task? Swipe through for a visual introduction to one of the most interesting approaches in interpretable machine learning. Save this carousel if you’re learning **Explainable AI**, and share it with someone working on interpretable ML. #ConceptBottleneckModels #ExplainableAI #XAI #MachineLearning #ArtificialIntelligence

#cup cut #

IG:ansoni.de🧃#fyp #foryou #frase

IG:ansoni.de🧃#fyp #foryou #frase

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