Understanding Deep Learning Research Tutorial - Theory, Code and Math скачать в хорошем качестве

Understanding Deep Learning Research Tutorial - Theory, Code and Math

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. ❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba) ⭐️ 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 🎉 Thanks to our Champion and Sponsor supporters: 👾 Drake Milly 👾 Ulises Moralez 👾 Goddard Tan 👾 David MG 👾 Matthew Springman 👾 Claudio 👾 Oscar R. 👾 jedi-or-sith 👾 Nattira Maneerat 👾 Justin Hual -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news