Technical Details of Attention Mechanism in Transformers and LLMs

Attention and transformers in LLMs, the five stages of data processing, and a brand-new Large Language Models A-Z course: Kirill Eremenko joins host Jon Krohn to explore what goes into well-crafted LLMs, what makes Transformers so powerful, and how to succeed as a data scientist in this new age of generative AI.

This episode is brought to you by Intel and HPE Ezmeral Software Solutions, and by Prophets of AI, the leading agency for AI experts. Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.

In this episode you will learn:
• Supply and demand in AI recruitment [08:30]
• Kirill and Hadelin's new course on LLMs, “Large Language Models (LLMs), Transformers & GPT A-Z” [15:37]
• The learning difficulty in understanding LLMs [19:46]
• The basics of LLMs [22:00]
• The five building blocks of transformer architecture [36:29]
- 1: Input embedding [44:10]
- 2: Positional encoding [50:46]
- 3: Attention mechanism [54:04]
- 4: Feedforward neural network [1:16:17]
- 5: Linear transformation and softmax [1:19:16]
• Inference vs training time [1:29:12]
• Why transformers are so powerful [1:49:22]

Additional materials: www.superdatascience.com/747