GPU by hand ✍️ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more 👇

CPU
• It has one core.
• Its global memory has 120 locations (0-119).
• To use the GPU, it needs to copy data from the global memory to the GPU.
• After GPU is done, it will copy the results back.

GPU
• It has four cores to run four threads (0-3).
• It has a register file of 28 locations (0-27)
• This register file has four banks (0-3).
• All threads share the same register file.
• But they must read/write using the four banks.
• Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.

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What is torch.nn really?

When I started working with PyTorch, my biggest question was: "What is torch.nn?".

This article explains it quite well.

📌 Read

#pytorch #AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers

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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

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

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

🥇 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

𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝗳𝗼𝗿 𝗝𝗼𝗯 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀.

In DS or AI/ML interviews, you need to be able to explain models, debug them live, and design AI/ML systems from scratch. If you can’t demonstrate this during an interview, expect to hear, “We’ll get back to you.”

The attached person's name is Chip Huyen. Hopefully you know her; if not, then I can't help you here. She is probably one of the finest authors in the field of AI/ML.

She designed proper documentation/a book for common ML interview questions.

Target Audiences: ML engineer, a platform engineer, a research scientist, or you want to do ML but don’t yet know the differences among those titles.Check the comment section for links and repos.

📌 link:
https://huyenchip.com/ml-interviews-book/

#JobInterview #MachineLearning #AI #DataScience #MLEngineer #AIInterview #TechCareers #DeepLearning #AICommunity #MLSystems #CareerGrowth #AIJobs #ChipHuyen #InterviewPrep #DataScienceCommunit


https://t.me/CodeProgrammer 🌟