If you've ever felt intimidated by deep learning research papers with their dense mathematical notation and complex code bases, this comprehensive tutorial from @deeplearningexplained will show you how to effectively understand and implement cutting-edge AI research. Through practical examples using recent papers, you'll learn the three essential skills needed to master deep learning research: reading technical papers, understanding mathematical notation, and navigating research code bases.
⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:57) Section 1 - How to read research paper?
⌨️ (0:03:49) Section 1 - Step 1 Get External Context
⌨️ (0:04:51) Section 1 - Step 2 First Casual Read
⌨️ (0:06:01) Section 1 - Step 3 Fill External Gap
⌨️ (0:06:28) Section 1 - Step 4 Conceptual Understanding
⌨️ (0:07:41) Section 1 - Step 5 Code Deep Dive
⌨️ (0:08:29) Section 1 - Step 6 Method and Result Slow Walk
⌨️ (0:09:56) Section 1 - Step 7 Weird Gap Identification
⌨️ (0:10:28) Section 2 - How to read Deep Learning Math?
⌨️ (0:11:22) Section 2 - Step 0 : relax
⌨️ (0:12:02) Section 2 - Step 1 : identify all formula shown or referred
⌨️ (0:12:38) Section 2 - Step 2 : take the formulas out of the digital world
⌨️ (0:13:07) Section 2 - Step 3 : work on them to translate symbols into meaning (QHAdam)
⌨️ (0:36:57) Section 2 - Step 4 : summarize the meanings into an intuition
⌨️ (0:37:25) Section 3 - How to learn math efficiently
⌨️ (0:44:31) Section 3 - Step 1 - Select the right math sub field
⌨️ (0:45:03) Section 3 - Step 2 - Find exercise-rich resource
⌨️ (0:45:23) Section 3 - Step 3 - green, yellow and red method
⌨️ (0:48:09) Section 3 - Step 4 - study the theory to fix yellow and red
⌨️ (0:49:49) Section 4 - How to read deep learning codebase?
⌨️ (0:50:25) Section 4 - Step 0 Read the paper
⌨️ (0:50:47) Section 4 - Step 1 Run the code
⌨️ (0:53:16) Section 4 - Step 2 Map the codebase structure
⌨️ (0:56:47) Section 4 - Step 3 Elucidate all the components
⌨️ (1:03:13) Section 4 - Step 4 Take notes of unclear elements
⌨️ (1:03:41) Section 5 - Segment Anything Model Deep Dive
⌨️ (1:04:27) Section 5 - Task
⌨️ (1:08:50) Section 5 - SAM Testing
⌨️ (1:13:32) Section 5 - Model Theory
⌨️ (1:17:14) Section 5 - Model Code Overview
⌨️ (1:23:46) Section 5 - Image Encoder Code
⌨️ (1:25:25) Section 5 - Prompt Encoder Code
⌨️ (1:28:33) Section 5 - Mask Decoder Code
⌨️ (1:40:21) Section 5 - Data & Engine
⌨️ (1:42:47) Section 5 - Zero-Shot Results
⌨️ (1:45:21) Section 5 - Limitation
⌨️ (1:45:53) Conclusion
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