🔥 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

#VisionTransformer #ViT #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #MCQ #InterviewPrep

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🧹 ObjectClear — an AI-powered tool for removing objects from images effortlessly.

⚙️ What It Can Do:

🖼️ Upload any image
🎯 Select the object you want to remove
🌟 The model automatically erases the object and intelligently reconstructs the background

⚡️ Under the Hood:

— Uses Segment Anything (SAM) by Meta for object segmentation
— Leverages Inpaint-Anything for realistic background generation
— Works in your browser with an intuitive Gradio UI

✔️ Fully open-source and can be run locally.

📎 GitHub: https://github.com/zjx0101/ObjectClear

#AI #ImageEditing #ComputerVision #Gradio #OpenSource #Python

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🚀 Comprehensive Tutorial: Build a Folder Monitoring & Intruder Detection System in Python

In this comprehensive, step-by-step tutorial, you will learn how to build a real-time folder monitoring and intruder detection system using Python.

🔐 Your Goal:
Create a background program that:
- Monitors a specific folder on your computer.
- Instantly captures a photo using the webcam whenever someone opens that folder.
- Saves the photo with a timestamp in a secure folder.
- Runs automatically when Windows starts.
- Keeps running until you manually stop it (e.g., via Task Manager or a hotkey).

Read and get code: https://hackmd.io/@husseinsheikho/Build-a-Folder-Monitoring

#Python #Security #FolderMonitoring #IntruderDetection #OpenCV #FaceCapture #Automation #Windows #TaskScheduler #ComputerVision

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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Comprehensive Guide: How to Prepare for an Image Processing Job Interview – 500 Most Common Interview Questions

Let's start: https://hackmd.io/@husseinsheikho/IP

#ImageProcessing #ComputerVision #OpenCV #Python #InterviewPrep #DigitalImageProcessing #MachineLearning #AI #SignalProcessing #ComputerGraphics

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A useful find on GitHub CheatSheets-for-Developers

LINK: https://github.com/crescentpartha/CheatSheets-for-Developers

This is a huge collection of cheat sheets for a wide variety of technologies:

JavaScript, Python, Git, Docker, SQL, Linux, Regex, and many others.

Conveniently structured — you can quickly find the topic you need.

Save it and use it 🔥

👉 @DATASCIENCEN

This repo is awesome. It features RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more.

✅ link: https://github.com/Shubhamsaboo/awesome-llm-apps

#RAG #AIAgents #MultiAgentSystems #VoiceAI #LLMApps

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500 Essential Web Scraping Interview Questions

Start: https://hackmd.io/@husseinsheikho/WS-Interview

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

500 Essential Web Scraping Interview Questions with Answers - part 1 (1 to 238)

Link: https://hackmd.io/@husseinsheikho/WS-Ans238

500 Essential Web Scraping Interview Questions with Answers - part 2 (239 to 386)

Link: https://hackmd.io/@husseinsheikho/WS-Ans386

500 Essential Web Scraping Interview Questions with Answers - part 3 (387 to 500)

Link: https://hackmd.io/@husseinsheikho/WS-Ans500

https://t.me/DataScienceQ ✅

This repository contains a collection of everything needed to work with libraries related to AI and LLM.

More than 120 libraries, sorted by stages of LLM development:

→ Training, fine-tuning, and evaluation of LLM models
→ Integration and deployment of applications with LLM and RAG
→ Fast and scalable model launching
→ Working with data: extraction, structuring, and synthetic generation
→ Creating autonomous agents based on LLM
→ Prompt optimization and ensuring safe use in production

🌟 link: https://github.com/Shubhamsaboo/awesome-llm-apps

👉 @codeprogrammer

🥇 This repo is like gold for every data scientist!

✅ Just open your browser; a ton of interactive exercises and real experiences await you. Any question about statistics, probability, Python, or machine learning, you'll get the answer right there! With code, charts, even animations. This way, you don't waste time, and what you learn really sticks in your mind!

⬅️ Data science statistics and probability topics
⬅️ Clustering
⬅️ Principal Component Analysis (PCA)
⬅️ Bagging and Boosting techniques
⬅️ Linear regression
⬅️ Neural networks and more...


┌ 📂 Int Data Science Python Dash
└ 🐱 GitHub-Repos

👉 @codeprogrammer

#Python #OpenCV #Automation #ML #AI #DEEPLEARNING #MACHINELEARNING #ComputerVision

Want to learn Python quickly and from scratch? Then here’s what you need — CodeEasy: Python Essentials

🔹Explains complex things in simple words
🔹Based on a real story with tasks throughout the plot
🔹Free start

Ready to begin? Click https://codeeasy.io/course/python-essentials 🌟

👉 @DataScience4

Must watch "AI Engineer YouTube Playlist"

1. Neural Networks Zero to Hero (Karpathy) - https://lnkd.in/gBVSQqFf

2. Language Modelling from Scratch (Stanford CS336 2025) - https://lnkd.in/guuhQ8gA

3. Introduction to Deep Learning (MIT 6.S191 2025) - https://lnkd.in/ggBB_aCm

4. Introduction to Transformers (Talk - Andrej Karpathy) - https://lnkd.in/gYMTVVmH

5. Building LLMs (Stanford CS229 Guest Lecture) - https://lnkd.in/gP9xqXxi

6. Deep Dive into LLMs like ChatGPT - https://lnkd.in/gtZ9BAdA

7. Let’s Build GPT from Scratch - https://lnkd.in/gdNj7_Tw

8. Agentic AI by Stanford - https://lnkd.in/gknxmPQG

9. Transformers and Self-Attention - https://lnkd.in/gvZZtciU

https://t.me/CodeProgrammer ✈️

800+ Data Science Interview Questions – A Must-Have Resource for Every Aspirant

Breaking into the data science field is challenging—not because of a lack of opportunities, but because of how thoroughly you need to prepare.

This document, curated by Steve Nouri, is a goldmine of 800+ real-world interview questions covering:

-Statistics
-Data Science Fundamentals
-Data Analysis
-Machine Learning
-Deep Learning
-Python & R
-Model Evaluation & Optimization
-Deployment Strategies
…and much more!

https://t.me/CodeProgrammer 🔰

Top 140 PyTorch Interview Questions and Answers

This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.

🧠 Link: https://hackmd.io/@husseinsheikho/pytorch-interview

https://t.me/CodeProgrammer

“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.

✅ Videos:

Everything here ⇒ https://lnkd.in/ePfB8_rk

➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn

✅ Courses:

All Courses here ⇒ https://lnkd.in/eKKs9ves

➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9

✅ Guides:

Access all ⇒ https://lnkd.in/e-GA-HRh

➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh

✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX

✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE

Access all ⇒ https://lnkd.in/e-GA-HRh

By: https://t.me/CodeProgrammer 🟡