Positional Encoding Strategies in Transformer-Based Generative AI
Explore key positional encoding strategies in Transformer-based Generative AI, including Sinusoidal, RoPE, and ALiBi. Learn how these methods enable models to understand sequence order and handle long contexts effectively.
Positional Encodings in LLMs: How Transformers Understand Word Order
Discover how positional encodings enable transformers to understand word order. We compare sinusoidal, learned, and RoPE methods used in LLMs like Llama 3.
Residual Connections and Layer Normalization in Large Language Models: Why They Keep Training Stable
Residual connections and layer normalization are essential for training stable, deep large language models. Without them, transformers couldn't scale beyond a few layers. Here's how they work and why they're non-negotiable in modern AI.
Multimodal Transformer Foundations: How Text, Image, Audio, and Video Embeddings Are Aligned
Multimodal transformers align text, image, audio, and video into a shared embedding space, enabling systems to understand the world like humans do. Learn how they work, where they're used, and why audio remains the hardest modality to master.
Transformer Pre-Norm vs Post-Norm Architectures: Which One Keeps LLMs Stable?
Pre-norm and post-norm architectures determine how Layer Normalization is applied in Transformers. Pre-norm enables stable training of deep LLMs with 100+ layers, while post-norm struggles beyond 30 layers. Most modern models like GPT-4 and Llama 3 use pre-norm.
