Scaling AI Models with Mixture of Experts (MOE) Design Principles and Real– World Applications
Free Download 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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