The content discusses the basics of transformers in machine learning, focusing on the self-attention mechanism, advantages of transformers over traditional RNNs and LSTMs, and the concept of large language models (LLMs) like GPT-3 and BERT. It explains the differences in training objectives and architectures between GPT and BERT, as well as how to evaluate the performance of an LLM on a specific NLP task. The concept of transfer learning in LLMs is also explored, along with common challenges in deploying LLMs in production, such as resource consumption, latency, bias and fairness issues, security concerns, and maintenance complexities. The article aims to provide a comprehensive overview of these topics for readers interested in understanding and working with transformers and LLMs in machine learning.
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Source link: https://sinchanabhat.medium.com/learn-llm-basics-33133bda731a?source=rss——llm-5
Brushing up on LLM basics on a lazy Saturday. #LawSchoolEssentials
