In this beginner-friendly explainer video, we break down the transformer architecture that powers today’s leading generative AI models like ChatGPT, Gemini, and Claude. You’ll learn how transformers improved on RNNs, how self-attention and positional encoding work, and why this architecture is the key to large language models (LLMs).
Whether you’re just getting started with AI or want a solid understanding of how LLMs generate text, this video has you covered.
🌟***OTHER VIDEOS YOU MIGHT ENJOY***🌟
• What is a neural network? https://youtu.be/YKcF6L-0jRo
• Fine-tuning vs. RAG: https://youtu.be/L7PfLk4a2oY
🌟***TIMESTAMPS***🌟
00:00 – What is a transformer as it relates to generative AI?
01:16 – How do recurrent neural networks (RNNs) fit into AI?
01:39 – What are the limitations of RNNs?
02:13 – Why the transformer architecture is an improvement over RNNs
02:26 – How does positional encoding work with transformers?
02:51 – What is attention or self-attention as it relates to transformers?
03:39 – The transformer architecture enables large language models (LLMs)
03:59 – How LLMs work to predict the word that comes next in a sentence
05:20 – Summarizing large language models and the transformer architecture
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