@user3361478553154: 🫵🌠 إلابداع والمبدعين ذكرى خالدة عندليب نجد الراحل عبدالله الصريخ ابو سليمان الله يرحمه ويغفر له ☝️

🫵 فهد 🌠 🇸🇦 🌠

Open In TikTok:

Region: SA

Sunday 04 October 2026 22:33:54 GMT

7680

134

12

26

Music

Download

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

Comments

العندليب عبدالعزيز 🇸🇦🎤 :

الله يرحمه ويغفر له عبدالله الصريخ مبدع 🌹🌹

2026-10-05 06:09:23

2

user2034410985986A :

ألله يرحمك الفنان عبدالله الصريخ ابوسليمان 💐🇸🇦🇰🇼

2026-10-05 06:56:18

0

هاوي المقناص احمد ابو فهد :

الله يرحمك يالصريخ فقدناك

2026-10-06 05:20:25

0

بدوي متحضر :

الله يرحمه عز الله ما بعده فنان الله أكبر يازمن .. لن أنس هذا الشريط وهذه الأغنية بالذات في عصرية من صيف ١٤٠٨ بصحراء غرب من طريف قرب الحدود الاردنية مع رفقة كرام الله يذكرهم بالخير

2026-10-06 22:20:26

0

Ok :

يامل الجنه 🥺🥺

2026-10-05 04:44:19

0

☾ ♛ Al harbi ♛ ☽ :

2026-10-05 15:55:40

0

نٌےـوُآفُےـ иαωαf :

❤️❤️❤️

2026-10-05 12:19:03

0

ابو زياد عنيزة :

الله يرحمك ويغفر للك

2026-10-05 10:42:41

0

محمد :

الله يرحمه أفضل فناني بس كويس ما كان فيه تسجيل فيديو وقته

2026-10-07 19:57:41

0

فهد :

[وردة]

2026-10-05 00:47:53

0

طلال الحرمان :

🥰🥰🥰

2026-10-04 22:50:39

0

نايف الشمري :

😭😭😭

2026-10-05 17:01:33

0

To see more videos from user @user3361478553154, please go to the Tikwm homepage.

Other Videos

Computer Vision (CV) is a field of Artificial Intelligence that enables computers to extract meaningful information from images and videos. From recognizing faces to analyzing medical scans, Computer Vision turns visual data into actionable insights. 🧠 HOW COMPUTER VISION WORKS 🖼️ Image / Video ⬇️ 🧹 Preprocessing Resize, normalize, or enhance the input. ⬇️ 🔍 Feature Extraction Identify useful visual patterns such as edges, textures, shapes, and objects. ⬇️ 🧠 Model A deep learning model analyzes the visual features. ⬇️ 🎯 Prediction The system identifies, locates, segments, or generates information about the visual content. 🔥 CORE COMPUTER VISION TASKS 1️⃣ IMAGE CLASSIFICATION 🏷️ What is in the image? Example: 🐱 Cat 🐶 Dog 🚗 Car 2️⃣ OBJECT DETECTION 🎯 What objects are present and where are they? The model identifies objects and draws bounding boxes around them. Used in: 🚗 Autonomous Vehicles 📹 Surveillance 🏭 Manufacturing 3️⃣ IMAGE SEGMENTATION ✂️ Which pixels belong to which object? Used in: 🏥 Medical Imaging 🚗 Autonomous Driving 🛰️ Satellite Analysis 4️⃣ FACE RECOGNITION 👤 Identifies or verifies people based on facial features. Used in: 🔐 Authentication 📱 Device Security 🛂 Identity Verification 5️⃣ OCR 📝 Optical Character Recognition Converts text inside images into machine-readable text. 📄 Scanned Documents 🪪 ID Cards 🧾 Receipts 🧠 POPULAR MODELS 🖼️ CNNs → Visual feature extraction 🎯 YOLO → Real-time object detection 🔥 ResNet → Deep image classification 👁️ Vision Transformers (ViT) → Transformer-based vision ✂️ U-Net → Image segmentation 🛠️ POPULAR TOOLS 🐍 Python 📷 OpenCV 🔥 PyTorch 🧠 TensorFlow 🤗 Hugging Face 📊 NumPy 🌍 REAL-WORLD APPLICATIONS 🏥 Medical Imaging 🚗 Autonomous Vehicles 🔐 Face Authentication 🏭 Quality Inspection 🛰️ Satellite Imagery 🛒 Retail Analytics 📱 Augmented Reality 🤖 Robotics 🧩 COMPUTER VISION PIPELINE Image → Preprocess → Extract Features → Model → Prediction → Decision 💡 Computer Vision isn’t simply about recognizing pictures. It’s about converting pixels into information that machines can understand and use. And with modern Vision Transformers and multimodal models, AI can increasingly combine vision + language + reasoning in a single system. 🚀 #ComputerVision #AI #ArtificialIntelligence #creatorsearchinsights #computerengineer

Computer Vision (CV) is a field of Artificial Intelligence that enables computers to extract meaningful information from images and videos. From recognizing faces to analyzing medical scans, Computer Vision turns visual data into actionable insights. 🧠 HOW COMPUTER VISION WORKS 🖼️ Image / Video ⬇️ 🧹 Preprocessing Resize, normalize, or enhance the input. ⬇️ 🔍 Feature Extraction Identify useful visual patterns such as edges, textures, shapes, and objects. ⬇️ 🧠 Model A deep learning model analyzes the visual features. ⬇️ 🎯 Prediction The system identifies, locates, segments, or generates information about the visual content. 🔥 CORE COMPUTER VISION TASKS 1️⃣ IMAGE CLASSIFICATION 🏷️ What is in the image? Example: 🐱 Cat 🐶 Dog 🚗 Car 2️⃣ OBJECT DETECTION 🎯 What objects are present and where are they? The model identifies objects and draws bounding boxes around them. Used in: 🚗 Autonomous Vehicles 📹 Surveillance 🏭 Manufacturing 3️⃣ IMAGE SEGMENTATION ✂️ Which pixels belong to which object? Used in: 🏥 Medical Imaging 🚗 Autonomous Driving 🛰️ Satellite Analysis 4️⃣ FACE RECOGNITION 👤 Identifies or verifies people based on facial features. Used in: 🔐 Authentication 📱 Device Security 🛂 Identity Verification 5️⃣ OCR 📝 Optical Character Recognition Converts text inside images into machine-readable text. 📄 Scanned Documents 🪪 ID Cards 🧾 Receipts 🧠 POPULAR MODELS 🖼️ CNNs → Visual feature extraction 🎯 YOLO → Real-time object detection 🔥 ResNet → Deep image classification 👁️ Vision Transformers (ViT) → Transformer-based vision ✂️ U-Net → Image segmentation 🛠️ POPULAR TOOLS 🐍 Python 📷 OpenCV 🔥 PyTorch 🧠 TensorFlow 🤗 Hugging Face 📊 NumPy 🌍 REAL-WORLD APPLICATIONS 🏥 Medical Imaging 🚗 Autonomous Vehicles 🔐 Face Authentication 🏭 Quality Inspection 🛰️ Satellite Imagery 🛒 Retail Analytics 📱 Augmented Reality 🤖 Robotics 🧩 COMPUTER VISION PIPELINE Image → Preprocess → Extract Features → Model → Prediction → Decision 💡 Computer Vision isn’t simply about recognizing pictures. It’s about converting pixels into information that machines can understand and use. And with modern Vision Transformers and multimodal models, AI can increasingly combine vision + language + reasoning in a single system. 🚀 #ComputerVision #AI #ArtificialIntelligence #creatorsearchinsights #computerengineer

#funnyanimals #funnyvideo #pet #fyp

#funnyanimals #funnyvideo #pet #fyp

Generative AI models can create new content from learned patterns, including text, images, audio, video, and code. But not every model works the same way. Here’s the quick map. 👇 1️⃣ LARGE LANGUAGE MODELS (LLMs) 💬 Generate and understand text, code, and other token-based content. Examples: 🤖 GPT 🦙 Llama 🌟 Gemini 🧠 Claude 💻 Mistral Used for: 📝 Text generation 💬 Chatbots 💻 Code generation 🔎 RAG applications 🤖 AI agents 2️⃣ DIFFUSION MODELS 🎨 Generate content by learning to transform noise into meaningful samples. Examples: 🎨 Stable Diffusion 🖼️ FLUX 🎬 Video diffusion models Used for: 🖼️ Image generation 🎨 Image editing 🎬 Video generation 3️⃣ GANs 🤝 Generative Adversarial Networks use two networks: 🎨 Generator → Creates samples 🕵️ Discriminator → Tries to distinguish generated samples from real ones Used for: 🖼️ Image generation ✨ Image enhancement 🧪 Synthetic data 4️⃣ VAEs 🧩 Variational Autoencoders learn a compact latent representation of data and can generate new samples from that learned space. Used for: 🔄 Data generation 🧠 Representation learning 🎨 Image generation 📉 Dimensionality reduction 5️⃣ MULTIMODAL MODELS 🌐 Work across multiple types of information. They can combine: 📝 Text 🖼️ Images 🎙️ Audio 🎬 Video 💻 Code Used for: 👁️ Vision + language 💬 Image understanding 📄 Document analysis 🤖 Multimodal AI assistants 6️⃣ TEXT-TO-SPEECH MODELS 🎙️ Generate natural-sounding speech from text. Used for: 🎧 AI voice assistants 📚 Audiobooks 🎮 Voice characters ♿ Accessibility tools 7️⃣ MUSIC & AUDIO GENERATION 🎵 Generate or transform audio using learned patterns. Used for: 🎵 Music generation 🔊 Sound effects 🎙️ Audio creation 🎬 Media production 🧠 QUICK COMPARISON 💬 LLMs → Text & Code 🎨 Diffusion → Images & increasingly video/audio 🤝 GANs → Synthetic data & image generation 🧩 VAEs → Latent representations & generation 🌐 Multimodal Models → Text + Vision + Audio + More 🎙️ TTS Models → Speech 🎵 Audio Models → Music & Sound 🚀 GENAI PIPELINE Data → Training → Learned Representation → Generation → Evaluation → Application And in real-world applications, models are often combined with: 🔎 RAG 🛠️ Tools 🤖 Agents 🗄️ Databases ⚙️ APIs 💡 The important skill isn’t memorizing model names. Understand what type of data a model consumes, what it generates, how it works, and where it fits in a real AI system. Save this GenAI cheat sheet. 📌 #GenerativeAI #GenAI #AI #ArtificialIntelligence #creatorsearchinsights

Generative AI models can create new content from learned patterns, including text, images, audio, video, and code. But not every model works the same way. Here’s the quick map. 👇 1️⃣ LARGE LANGUAGE MODELS (LLMs) 💬 Generate and understand text, code, and other token-based content. Examples: 🤖 GPT 🦙 Llama 🌟 Gemini 🧠 Claude 💻 Mistral Used for: 📝 Text generation 💬 Chatbots 💻 Code generation 🔎 RAG applications 🤖 AI agents 2️⃣ DIFFUSION MODELS 🎨 Generate content by learning to transform noise into meaningful samples. Examples: 🎨 Stable Diffusion 🖼️ FLUX 🎬 Video diffusion models Used for: 🖼️ Image generation 🎨 Image editing 🎬 Video generation 3️⃣ GANs 🤝 Generative Adversarial Networks use two networks: 🎨 Generator → Creates samples 🕵️ Discriminator → Tries to distinguish generated samples from real ones Used for: 🖼️ Image generation ✨ Image enhancement 🧪 Synthetic data 4️⃣ VAEs 🧩 Variational Autoencoders learn a compact latent representation of data and can generate new samples from that learned space. Used for: 🔄 Data generation 🧠 Representation learning 🎨 Image generation 📉 Dimensionality reduction 5️⃣ MULTIMODAL MODELS 🌐 Work across multiple types of information. They can combine: 📝 Text 🖼️ Images 🎙️ Audio 🎬 Video 💻 Code Used for: 👁️ Vision + language 💬 Image understanding 📄 Document analysis 🤖 Multimodal AI assistants 6️⃣ TEXT-TO-SPEECH MODELS 🎙️ Generate natural-sounding speech from text. Used for: 🎧 AI voice assistants 📚 Audiobooks 🎮 Voice characters ♿ Accessibility tools 7️⃣ MUSIC & AUDIO GENERATION 🎵 Generate or transform audio using learned patterns. Used for: 🎵 Music generation 🔊 Sound effects 🎙️ Audio creation 🎬 Media production 🧠 QUICK COMPARISON 💬 LLMs → Text & Code 🎨 Diffusion → Images & increasingly video/audio 🤝 GANs → Synthetic data & image generation 🧩 VAEs → Latent representations & generation 🌐 Multimodal Models → Text + Vision + Audio + More 🎙️ TTS Models → Speech 🎵 Audio Models → Music & Sound 🚀 GENAI PIPELINE Data → Training → Learned Representation → Generation → Evaluation → Application And in real-world applications, models are often combined with: 🔎 RAG 🛠️ Tools 🤖 Agents 🗄️ Databases ⚙️ APIs 💡 The important skill isn’t memorizing model names. Understand what type of data a model consumes, what it generates, how it works, and where it fits in a real AI system. Save this GenAI cheat sheet. 📌 #GenerativeAI #GenAI #AI #ArtificialIntelligence #creatorsearchinsights

Gia Tài Của Cụ Phến - Tập 8 | #phamvinh99 #vulaci #Lifebuoy #LifebuoyVietnam #HợpTácCùngUnilever

Gia Tài Của Cụ Phến - Tập 8 | #phamvinh99 #vulaci #Lifebuoy #LifebuoyVietnam #HợpTácCùngUnilever

Probability distributions describe how probabilities are assigned to possible values of a random variable. There are two fundamental types you should know. 👇 1️⃣ DISCRETE DISTRIBUTION 🔢 A discrete random variable takes countable values, often integers. Examples: 🎲 Number on a dice 👥 Number of customers 📞 Number of calls 🪙 Number of heads PMF Probability Mass Function (PMF) gives the probability of each individual value. Example: 🎲 P(X = 3) = 1/6 Common discrete distributions: 🔹 Binomial 🔹 Poisson 🔹 Bernoulli 🔹 Geometric 2️⃣ CONTINUOUS DISTRIBUTION 📈 A continuous random variable can take any value within a range of real numbers. Examples: 📏 Height ⚖️ Weight 🌡️ Temperature ⏱️ Time 💰 Measurements PDF Probability Density Function (PDF) describes the density of probability across values. For continuous variables: P(X = exact value) = 0 Instead, probability is calculated over an interval. Example: P(170 < Height < 180) Common continuous distributions: 🔔 Normal ⏳ Exponential 📊 Uniform 🎯 Student’s t 🧠 EASY WAY TO REMEMBER PMF → Points / individual values PDF → Density across a continuous range 🔢 Discrete: “How likely is this exact outcome?” 📈 Continuous: “How much probability lies within this range?” 💡 Understanding probability distributions is essential for statistics, hypothesis testing, Machine Learning, and Data Science. 📌 Save this cheat sheet for your ML journey. #Probability #Statistics #DataScience #MachineLearning #creatorsearchinsights

Probability distributions describe how probabilities are assigned to possible values of a random variable. There are two fundamental types you should know. 👇 1️⃣ DISCRETE DISTRIBUTION 🔢 A discrete random variable takes countable values, often integers. Examples: 🎲 Number on a dice 👥 Number of customers 📞 Number of calls 🪙 Number of heads PMF Probability Mass Function (PMF) gives the probability of each individual value. Example: 🎲 P(X = 3) = 1/6 Common discrete distributions: 🔹 Binomial 🔹 Poisson 🔹 Bernoulli 🔹 Geometric 2️⃣ CONTINUOUS DISTRIBUTION 📈 A continuous random variable can take any value within a range of real numbers. Examples: 📏 Height ⚖️ Weight 🌡️ Temperature ⏱️ Time 💰 Measurements PDF Probability Density Function (PDF) describes the density of probability across values. For continuous variables: P(X = exact value) = 0 Instead, probability is calculated over an interval. Example: P(170 < Height < 180) Common continuous distributions: 🔔 Normal ⏳ Exponential 📊 Uniform 🎯 Student’s t 🧠 EASY WAY TO REMEMBER PMF → Points / individual values PDF → Density across a continuous range 🔢 Discrete: “How likely is this exact outcome?” 📈 Continuous: “How much probability lies within this range?” 💡 Understanding probability distributions is essential for statistics, hypothesis testing, Machine Learning, and Data Science. 📌 Save this cheat sheet for your ML journey. #Probability #Statistics #DataScience #MachineLearning #creatorsearchinsights

About

Robot
API

Legal

Privacy Policy