[MAI554] Transformers for Language Modeling | Transformer Block and Architecture

Описание: Transformers for Language Modeling: Transformer Block and Architecture | Prof. Anis Koubaa | MAI554 Deep Learning for Language Modeling Course

In this video, Prof. Anis Koubaa dives into the core concepts of the Transformer architecture for language modeling, exploring key components like masked self-attention, multihead attention, and the crucial role of the Feed-Forward Network (FNN) in the transformer block. 🧠✨

We also discuss the importance of normalization and residual connections in enhancing the model’s performance, along with the innovative mixture of experts technique to improve efficiency. 💡

🔍 Topics Covered:
• Transformer Block Overview
• Masked Self-Attention
• Multihead Attention Mechanism
• The Role of Feed-Forward Networks
• Normalization & Residual Connections
• Mixture of Experts in Transformers

This lecture is part of the MAI554 Deep Learning for Language Modeling course at Alfaisal University. Don't forget to like, share, and subscribe for more deep learning insights! 🎓💻

#DeepLearning #Transformers #LanguageModeling #AI #MachineLearning #MultiheadAttention #FNN #Normalization #ResidualConnections #MixtureOfExperts