Understanding Deep Learning Research Tutorial – Theory, Code and Math

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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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