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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.
✉️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
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.
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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What is torch.nn really?
This article explains it quite well.
📌 Read
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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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#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience
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📚 JaidedAI/EasyOCR — an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.
### 🔍 Key Features:
🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects
### ✅ Example Usage:
### 📌 Ideal For:
✅ Text extraction from photos, scans, and documents
✅ Embedding OCR capabilities in apps (e.g. automated data entry)
🔗 GitHub: https://github.com/JaidedAI/EasyOCR
👉 Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
### 🔍 Key Features:
🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects
### ✅ Example Usage:
import easyocr
reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')
### 📌 Ideal For:
✅ Text extraction from photos, scans, and documents
✅ Embedding OCR capabilities in apps (e.g. automated data entry)
🔗 GitHub: https://github.com/JaidedAI/EasyOCR
👉 Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
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