

172: Transformers and Large Language Models
Mar 11, 2024
Exploring transformers and large language models in neural networks, focusing on latent variables, encoders, decoders, attention layers, and the history of RNNs. Discussing the vanishing gradient problem in LSTM and self-supervised learning with direct policy optimization.
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9
Introduction
00:00 • 2min
Exploring Remote Work and WeWork Spaces
01:31 • 22min
Exploring PID Controllers and Gemma Models in Large Language Models
23:27 • 5min
Exploring Model Parameters, Memory Usage, and Fine-Tuning in Large Language Models
28:32 • 3min
Exploring The Wheel of Time Series & TV Adaptation
31:03 • 3min
Exploring YouTube Recommendations and Game Development
33:49 • 15min
Challenges and Solutions in Neural Networks
48:23 • 25min
Evolution and Challenges in Scaling Language Models
01:13:07 • 10min
Speculating on the Future of Jobs in an AI-dominated World
01:23:25 • 3min