172: Transformers and Large Language Models
Programming Throwdown
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Challenges and Solutions in Neural Networks
The chapter explores the challenges faced by neural networks where processes tend to extremes, introducing LSTMs as a solution. It discusses the implementation difficulties and failures of LSTMs, transitioning into numerical differentiation and attention layers for better performance. The conversation progresses to self-attention in transformers, training large language models, and the complexities of generating coherent text with hidden Markov models, highlighting challenges and techniques in refining language models.
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Transcript
