Last month, Kirill Eremenko was on the show to detail Decoder-Only Transformers (like the GPT series). It was our most popular episode ever, so he's come right back today to detail an even more sophisticated architecture: Encoder-Decoder Transformers.
If you don’t already know him, Kirill:
• Is Founder and CEO of SuperDataScience, an e-learning platform that is the namesake of this podcast.
• Founded the Super Data Science Podcast in 2016 and hosted the show until he passed me the reins a little over three years ago.
• Has reached more than 2.7 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.
Kirill was most recently on the show for Episode #747 to provide a technical introduction to the Transformer module that underpins all the major modern Large Language Models (LLMs) like the GPT, Gemini, Llama and BERT architectures. We received an unprecedented amount of positive feedback from that episode, demanding more! So here we are.
That episode, #747, as well as today’s, are perhaps the two most technical episodes of this podcast ever so they probably appeal mostly to hands-on practitioners like data scientists and ML engineers, particularly those who already have some understanding of deep neural networks.
In this episode, Kirill:
• Reviews the key Transformer theory that we covered in Episode #747, namely the individual neural-network components of the Decoder-Only architecture that prevails in generative LLMs like the GPT series models.
• Builds on that to detail the full, Encoder-Decoder Transformer architecture that is used in the original Transformer by Google, in their “Attention is All You Need” paper, as well as in other models that excel at both natural-language understanding and generation such as T5 and BART.
• Discusses the performance and capability pros and cons of full Encoder-Decoder architectures relative to Decoder-Only architectures like GPT and Encoder-Only architectures like BERT.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.