SGD with Momentum and Adagrad | Generative Ai | Basic to Advance
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Welcome to Learning Logic with Prince Katiyar! Are you ready to transform your understanding of AI and elevate your tech career? Dive deep into the world of Stochastic Gradient Descent (SGD) with Momentum and uncover the powerful techniques that can resolve common issues like noise and saddle points in machine learning models. Discover the truth behind optimizing convergence speed and learn how previous data records can guide your path to success. Unlock your potential and solve complex problems with speed and precision. Explore now and don't miss out on these actionable insights that will empower your career journey. Watch now, subscribe to Learning Logic, and hit the bell icon for more #AI, #ArtificialIntelligence, #coding, and #techupdates. If you believe in these changes, share this video to reach as many people as possible!
Material Link : https://euron.one/course/generative-a...
CHAPTERS:
00:00 - Introduction
00:33 - Recap of Last Class
02:02 - Local Minima Problem
02:35 - Saddle Point Problem
03:06 - Noise Problem
03:35 - Faster Convergence
05:22 - Last Record
08:59 - Exponential Weighted Moving Average
09:58 - Financial Data Concept
11:29 - Velocity
15:20 - Last Point Importance
15:38 - Weighted Average Formula
17:14 - Gradient Descent with Momentum
20:32 - Negative of Old Data
22:01 - Space Value
25:07 - Importance of AdaGrad
25:38 - T and F Motion Challenges
26:00 - Handling Learning Rate
26:20 - Weight Update Formula
27:11 - Importance of Last Value in Backpropagation
27:47 - Importance of Past Value in Backpropagation
29:54 - Delta of Last Value in Backpropagation
31:08 - Loss Handling Challenges
32:55 - Next Class Plan
33:08 - Exponential Loss
34:14 - Problem Overview
34:29 - Local Minimum Solutions
34:44 - Speed Issues
35:10 - Exponential Weighted Average Explained
35:44 - Exponential Weighted Average Benefits
36:09 - Bias Correction
37:15 - Final Thoughts
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