🌟 Vision Transformer (ViT) Tutorial – Part 5: Efficient Vision Transformers – MobileViT, TinyViT & Edge Deployment
Read lesson: https://hackmd.io/@husseinsheikho/vit-5
#MobileViT #TinyViT #EfficientViT #EdgeAI #ModelOptimization #ONNX #TensorRT #TorchServe #DeepLearning #ComputerVision #Transformers
Read lesson: https://hackmd.io/@husseinsheikho/vit-5
#MobileViT #TinyViT #EfficientViT #EdgeAI #ModelOptimization #ONNX #TensorRT #TorchServe #DeepLearning #ComputerVision #Transformers
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🌟 Vision Transformer (ViT) Tutorial – Part 6: Vision Transformers in Production – MLOps, Monitoring & CI/CD
Learn more: https://hackmd.io/@husseinsheikho/vit-6
#MLOps #ModelMonitoring #CIforML #MLflow #WandB #Kubeflow #ProductionAI #DeepLearning #ComputerVision #Transformers #AIOps
Learn more: https://hackmd.io/@husseinsheikho/vit-6
#MLOps #ModelMonitoring #CIforML #MLflow #WandB #Kubeflow #ProductionAI #DeepLearning #ComputerVision #Transformers #AIOps
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🌟 Vision Transformer (ViT) Tutorial – Part 7: The Future of Vision Transformers – Multimodal, 3D, and Beyond
Learn: https://hackmd.io/@husseinsheikho/vit-7
#FutureOfViT #MultimodalAI #3DViT #TimeSformer #PaLME #MedicalAI #EmbodiedAI #RetNet #Mamba #NextGenAI #DeepLearning #ComputerVision #Transformers
Learn: https://hackmd.io/@husseinsheikho/vit-7
#FutureOfViT #MultimodalAI #3DViT #TimeSformer #PaLME #MedicalAI #EmbodiedAI #RetNet #Mamba #NextGenAI #DeepLearning #ComputerVision #Transformers
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🔥 Master Vision Transformers with 65+ MCQs! 🔥
Are you preparing for AI interviews or want to test your knowledge in Vision Transformers (ViT)?
🧠 Dive into 65+ curated Multiple Choice Questions covering the fundamentals, architecture, training, and applications of ViT — all with answers!
🌐 Explore Now: https://hackmd.io/@husseinsheikho/vit-mcq
🔹 Table of Contents
Basic Concepts (Q1–Q15)
Architecture & Components (Q16–Q30)
Attention & Transformers (Q31–Q45)
Training & Optimization (Q46–Q55)
Advanced & Real-World Applications (Q56–Q65)
Answer Key & Explanations
Are you preparing for AI interviews or want to test your knowledge in Vision Transformers (ViT)?
🧠 Dive into 65+ curated Multiple Choice Questions covering the fundamentals, architecture, training, and applications of ViT — all with answers!
🌐 Explore Now: https://hackmd.io/@husseinsheikho/vit-mcq
🔹 Table of Contents
Basic Concepts (Q1–Q15)
Architecture & Components (Q16–Q30)
Attention & Transformers (Q31–Q45)
Training & Optimization (Q46–Q55)
Advanced & Real-World Applications (Q56–Q65)
Answer Key & Explanations
#VisionTransformer #ViT #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #MCQ #InterviewPrep
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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PyTorch Masterclass: Part 2 – Deep Learning for Computer Vision with PyTorch
Duration: ~60 minutes
Link: https://hackmd.io/@husseinsheikho/pytorch-2
https://t.me/DataScienceM💯
Duration: ~60 minutes
Link: https://hackmd.io/@husseinsheikho/pytorch-2
#PyTorch #ComputerVision #CNN #DeepLearning #TransferLearning #CIFAR10 #ImageClassification #DataLoaders #Transforms #ResNet #EfficientNet #PyTorchVision #AI #MachineLearning #ConvolutionalNeuralNetworks #DataAugmentation #PretrainedModels
https://t.me/DataScienceM
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✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
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✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
✨ Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers ✨
📖 Table of Contents Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers Configuring Your Development Environment Problem Statement How Does Super-Resolution Solve This? State-of-the-Art Approaches Generative Adversarial Networks (GANs) Diffusion Models Implementing Diffus...
🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #Tutorial
📖 Table of Contents Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers Configuring Your Development Environment Problem Statement How Does Super-Resolution Solve This? State-of-the-Art Approaches Generative Adversarial Networks (GANs) Diffusion Models Implementing Diffus...
🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #Tutorial
✨ Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques ✨
📖 Table of Contents Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques Introduction Configuring Your Development Environment Need Help Configuring Your Development Environment? What Is Super-Resolution? Usual Problems with Low-Resolution Imagery Traditional Computer Vision A...
🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #TechnologyApplications #Tutorial
📖 Table of Contents Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques Introduction Configuring Your Development Environment Need Help Configuring Your Development Environment? What Is Super-Resolution? Usual Problems with Low-Resolution Imagery Traditional Computer Vision A...
🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #TechnologyApplications #Tutorial
✨ CycleGAN: Unpaired Image-to-Image Translation (Part 1) ✨
📖 Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 1) Introduction Unpaired Image Translation CycleGAN Pipeline and Training Loss Formulation Adversarial Loss Cycle Consistency Summary Citation Information CycleGAN: Unpaired Image-to-Image Translation (Part 1) In this tutorial, yo...
🏷️ #ComputerVision #CycleGAN #DeepLearning #Keras #KerasandTensorFlow #TensorFlow #UnpairedImageTranslation
📖 Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 1) Introduction Unpaired Image Translation CycleGAN Pipeline and Training Loss Formulation Adversarial Loss Cycle Consistency Summary Citation Information CycleGAN: Unpaired Image-to-Image Translation (Part 1) In this tutorial, yo...
🏷️ #ComputerVision #CycleGAN #DeepLearning #Keras #KerasandTensorFlow #TensorFlow #UnpairedImageTranslation
✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...
🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO
✨ People Tracker with YOLOv12 and Centroid Tracker ✨
📖 Table of Contents People Tracker with YOLOv12 and Centroid Tracker Introduction Why People Tracker Monitoring Matters How YOLOv12 Enables Real-Time Applications Configuring Your Development Environment Downloading the Input Video Install gdown Download the Video Visualizing the Inference and Trackin...
🏷️ #ComputerVision #ObjectDetection #PeopleTracker #Tutorial #YOLOv12
📖 Table of Contents People Tracker with YOLOv12 and Centroid Tracker Introduction Why People Tracker Monitoring Matters How YOLOv12 Enables Real-Time Applications Configuring Your Development Environment Downloading the Input Video Install gdown Download the Video Visualizing the Inference and Trackin...
🏷️ #ComputerVision #ObjectDetection #PeopleTracker #Tutorial #YOLOv12
✨ Meet BLIP: The Vision-Language Model Powering Image Captioning ✨
📖 Table of Contents Meet BLIP: The Vision-Language Model Powering Image Captioning What Is Image Captioning and Why Is It Challenging? Why It’s Challenging Why Traditional Vision Tasks Aren’t Enough Configuring Your Development Environment A Brief History of Image Captioning Models…...
🏷️ #ComputerVision #DeepLearning #ImageCaptioning #MultimodalAI #Tutorial
📖 Table of Contents Meet BLIP: The Vision-Language Model Powering Image Captioning What Is Image Captioning and Why Is It Challenging? Why It’s Challenging Why Traditional Vision Tasks Aren’t Enough Configuring Your Development Environment A Brief History of Image Captioning Models…...
🏷️ #ComputerVision #DeepLearning #ImageCaptioning #MultimodalAI #Tutorial
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🤖🧠 Thinking with Camera 2.0: A Powerful Multimodal Model for Camera-Centric Understanding and Generation
🗓️ 14 Oct 2025
📚 AI News & Trends
In the rapidly evolving field of multimodal AI, bridging gaps between vision, language and geometry is one of the frontier challenges. Traditional vision-language models excel at describing what is in an image “a cat on a sofa” “a red car on the road” but struggle to reason about how the image was captured: the camera’s ...
#MultimodalAI #CameraCentricUnderstanding #VisionLanguageModels #AIResearch #ComputerVision #GenerativeModels
🗓️ 14 Oct 2025
📚 AI News & Trends
In the rapidly evolving field of multimodal AI, bridging gaps between vision, language and geometry is one of the frontier challenges. Traditional vision-language models excel at describing what is in an image “a cat on a sofa” “a red car on the road” but struggle to reason about how the image was captured: the camera’s ...
#MultimodalAI #CameraCentricUnderstanding #VisionLanguageModels #AIResearch #ComputerVision #GenerativeModels
# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io
def process_product_image(s3_bucket, s3_key):
# 1. Download from S3
s3 = boto3.client('s3')
response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
img = Image.open(io.BytesIO(response['Body'].read()))
# 2. Standardize dimensions
img = img.convert("RGB")
img = img.resize((1200, 1200), Image.LANCZOS)
# 3. Remove background (simplified)
# In practice: use rembg or AWS Rekognition
img = remove_background(img)
# 4. Generate variants
variants = {
"web": img.resize((800, 800)),
"mobile": img.resize((400, 400)),
"thumbnail": img.resize((100, 100))
}
# 5. Upload to CDN
for name, variant in variants.items():
buffer = io.BytesIO()
variant.save(buffer, "JPEG", quality=95)
s3.upload_fileobj(
buffer,
"cdn-bucket",
f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
)
# 6. Generate WebP version
webp_buffer = io.BytesIO()
img.save(webp_buffer, "WEBP", quality=85)
s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")
process_product_image("user-uploads", "products/summer_dress.jpg")
By: @DataScienceM 👁
#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3
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In Python, building AI-powered Telegram bots unlocks massive potential for image generation, processing, and automation—master this to create viral tools and ace full-stack interviews! 🤖
Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots
https://t.me/DataScienceM🦾
# Basic Bot Setup - The foundation (PTB v20+ Async)
from telegram.ext import Application, CommandHandler, MessageHandler, filters
async def start(update, context):
await update.message.reply_text(
"✨ AI Image Bot Active!\n"
"/generate - Create images from text\n"
"/enhance - Improve photo quality\n"
"/help - Full command list"
)
app = Application.builder().token("YOUR_BOT_TOKEN").build()
app.add_handler(CommandHandler("start", start))
app.run_polling()
# Image Generation - DALL-E Integration (OpenAI)
import openai
from telegram.ext import ContextTypes
openai.api_key = os.getenv("OPENAI_API_KEY")
async def generate(update: Update, context: ContextTypes.DEFAULT_TYPE):
if not context.args:
await update.message.reply_text("❌ Usage: /generate cute robot astronaut")
return
prompt = " ".join(context.args)
try:
response = openai.Image.create(
prompt=prompt,
n=1,
size="1024x1024"
)
await update.message.reply_photo(
photo=response['data'][0]['url'],
caption=f"🎨 Generated: *{prompt}*",
parse_mode="Markdown"
)
except Exception as e:
await update.message.reply_text(f"🔥 Error: {str(e)}")
app.add_handler(CommandHandler("generate", generate))
Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots
#Python #TelegramBot #AI #ImageGeneration #StableDiffusion #OpenAI #MachineLearning #CodingInterview #FullStack #Chatbots #DeepLearning #ComputerVision #Programming #TechJobs #DeveloperTips #CareerGrowth #CloudComputing #Docker #APIs #Python3 #Productivity #TechTips
https://t.me/DataScienceM
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