Scaling Ai Models With Mixture Of Experts (moe) Design Principles And Real-World Applications

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Scaling Ai Models With Mixture Of Experts (moe): Design Principles And Real-World Applications
Released 10/2025
With Vaibhava Lakshmi Ravideshik
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 55m 51s | Size: 232 MB


Get a hands-on overview of Mixture of Experts (MoE) architecture, covering key design principles, implementation strategies, and real-world applications in scalable AI systems.
Course details
Mixture of Experts (MoE) is a cutting-edge neural network architecture that enables efficient model scaling by routing inputs through a small subset of expert subnetworks. In this course, instructor Vaibhava Lakshmi Ravideshik explores the inner workings of MoE, from its core components to advanced routing strategies like top-k gating. The course balances theoretical understanding with hands-on coding using PyTorch to implement a simplified MoE layer. Along the way, you'll also get a chance to review real-world applications of MoE in state-of-the-art models like GPT-4 and Mixtral.

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