Introduction to Deep Learning.pdf
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Introduction to Deep Learning
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.
Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.
What makes Deep Learning so powerful?
Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.
#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.
Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.
What makes Deep Learning so powerful?
Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.
#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
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GPU by hand โ๏ธ 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 ๐
CPU
โข It has one core.
โข Its global memory has 120 locations (0-119).
โข To use the GPU, it needs to copy data from the global memory to the GPU.
โข After GPU is done, it will copy the results back.
GPU
โข It has four cores to run four threads (0-3).
โข It has a register file of 28 locations (0-27)
โข This register file has four banks (0-3).
โข All threads share the same register file.
โข But they must read/write using the four banks.
โข Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
โ๏ธ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
CPU
โข It has one core.
โข Its global memory has 120 locations (0-119).
โข To use the GPU, it needs to copy data from the global memory to the GPU.
โข After GPU is done, it will copy the results back.
GPU
โข It has four cores to run four threads (0-3).
โข It has a register file of 28 locations (0-27)
โข This register file has four banks (0-3).
โข All threads share the same register file.
โข But they must read/write using the four banks.
โข 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.
๐ Read
โ๏ธ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well.
๐ 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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๐ JaidedAI/EasyOCR โ an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.
### ๐ Key Features:
๐ธ Extracts text from images and scanned documents โ including handwritten notes and unusual fonts
๐ธ Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
๐ธ Built on PyTorch โ uses modern deep learning models (not the old-school Tesseract)
๐ธ Simple to integrate into your Python projects
### โ Example Usage:
### ๐ Ideal For:
โ Text extraction from photos, scans, and documents
โ Embedding OCR capabilities in apps (e.g. automated data entry)
๐ GitHub: https://github.com/JaidedAI/EasyOCR
๐ Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
### ๐ Key Features:
๐ธ Extracts text from images and scanned documents โ including handwritten notes and unusual fonts
๐ธ Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
๐ธ Built on PyTorch โ uses modern deep learning models (not the old-school Tesseract)
๐ธ Simple to integrate into your Python projects
### โ Example Usage:
import easyocr
reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')
### ๐ Ideal For:
โ Text extraction from photos, scans, and documents
โ Embedding OCR capabilities in apps (e.g. automated data entry)
๐ GitHub: https://github.com/JaidedAI/EasyOCR
๐ Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
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Are you preparing for AI interviews or want to test your knowledge in Vision Transformers (ViT)?
Basic Concepts (Q1โQ15)
Architecture & Components (Q16โQ30)
Attention & Transformers (Q31โQ45)
Training & Optimization (Q46โQ55)
Advanced & Real-World Applications (Q56โQ65)
Answer Key & Explanations
#VisionTransformer #ViT #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #MCQ #InterviewPrep
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๐ Comprehensive Guide: How to Prepare for an Image Processing Job Interview โ 500 Most Common Interview Questions
Let's start: https://hackmd.io/@husseinsheikho/IP
#ImageProcessing #ComputerVision #OpenCV #Python #InterviewPrep #DigitalImageProcessing #MachineLearning #AI #SignalProcessing #ComputerGraphics
Let's start: https://hackmd.io/@husseinsheikho/IP
#ImageProcessing #ComputerVision #OpenCV #Python #InterviewPrep #DigitalImageProcessing #MachineLearning #AI #SignalProcessing #ComputerGraphics
โ๏ธ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐ Comprehensive Guide: How to Prepare for a Graph Neural Networks (GNN) Job Interview โ 350 Most Common Interview Questions
Read: https://hackmd.io/@husseinsheikho/GNN-interview
#GNN #GraphNeuralNetworks #MachineLearning #DeepLearning #AI #DataScience #PyTorchGeometric #DGL #NodeClassification #LinkPrediction #GraphML
Read: https://hackmd.io/@husseinsheikho/GNN-interview
#GNN #GraphNeuralNetworks #MachineLearning #DeepLearning #AI #DataScience #PyTorchGeometric #DGL #NodeClassification #LinkPrediction #GraphML
โ๏ธ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
๐ฑ Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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#Python #OpenCV #Automation #ML #AI #DEEPLEARNING #MACHINELEARNING #ComputerVision
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๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ผ๐ฏ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐.
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 canโt demonstrate this during an interview, expect to hear, โWeโll get back to you.โ
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 donโt yet know the differences among those titles.Check the comment section for links and repos.
๐ link:
https://huyenchip.com/ml-interviews-book/
๏ปฟ
https://t.iss.one/CodeProgrammer๐
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 canโt demonstrate this during an interview, expect to hear, โWeโll get back to you.โ
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 donโt yet know the differences among those titles.Check the comment section for links and repos.
https://huyenchip.com/ml-interviews-book/
#JobInterview #MachineLearning #AI #DataScience #MLEngineer #AIInterview #TechCareers #DeepLearning #AICommunity #MLSystems #CareerGrowth #AIJobs #ChipHuyen #InterviewPrep #DataScienceCommunit
๏ปฟ
https://t.iss.one/CodeProgrammer
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