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.

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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.

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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

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😉 A list of the best YouTube videos
✅ To learn data science


1️⃣ SQL language


⬅️ Learning

💰 4-hour SQL course from zero to one hundred

💰 Window functions tutorial

⬅️ Projects

📎 Starting your first SQL project

💰 Data cleansing project

💰 Restaurant order analysis

⬅️ Interview

💰 How to crack the SQL interview?

➖➖➖

2️⃣ Python


⬅️ Learning

💰 12-hour Python for Data Science course

⬅️ Projects

💰 Python project for beginners

💰 Analyzing Corona Data with Python

⬅️ Interview

💰 Python interview golden tricks

💰 Python Interview Questions

➖➖➖

3️⃣ Statistics and machine learning


⬅️ Learning

💰 7-hour course in applied statistics

💰 Machine Learning Training Playlist

⬅️ Projects

💰 Practical ML Project

⬅️ Interview

💰 ML Interview Questions and Answers

💰 How to pass a statistics interview?

➖➖➖

4️⃣ Product and business case studies


⬅️ Learning

💰 Building strong product understanding

💰 Product Metric Definition

⬅️ Interview

💰 Case Study Analysis Framework

💰 How to shine in a business interview?

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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

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

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

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🥇 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/

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