An Open Course on LLMs, Led by Practitioners
A new free course, "Mastering LLMs," offers over 40 hours of content on large language models, featuring workshops by 25 experts, aimed at enhancing AI product development for technical individuals.
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A new free course titled "Mastering LLMs" has been launched, offering workshops and talks led by over 25 industry experts in fields such as information retrieval, machine learning, and data science. The course is designed for technical individuals, including engineers and data scientists, who have some familiarity with large language models (LLMs) and are looking to enhance their AI product development skills. Topics covered include evaluations, retrieval-augmented generation (RAG), and fine-tuning. The course features more than 40 hours of content organized by subject area, with chapter summaries, notes, and additional resources to facilitate learning. Participants are encouraged to apply the knowledge gained to personal projects for better understanding. Testimonials from previous students highlight the course's practical insights, diverse perspectives from speakers, and the supportive community on Discord. The course aims to provide a comprehensive overview of LLMs without delving deeply into coding, making it accessible to a broader audience interested in AI.
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GitHub – Karpathy/LLM101n: LLM101n: Let's Build a Storyteller
The GitHub repository "LLM101n: Let's build a Storyteller" offers a course on creating a Storyteller AI Large Language Model using Python, C, and CUDA. It caters to beginners, covering language modeling, deployment, programming, data types, deep learning, and neural nets. Additional chapters and appendices are available for further exploration.
LLMs on the Command Line
Simon Willison presented a Python command-line utility for accessing Large Language Models (LLMs) efficiently, supporting OpenAI models and plugins for various providers. The tool enables running prompts, managing conversations, accessing specific models like Claude 3, and logging interactions to a SQLite database. Willison highlighted using LLM for tasks like summarizing discussions and emphasized the importance of embeddings for semantic search, showcasing LLM's support for content similarity queries and extensibility through plugins and OpenAI API compatibility.
LLMs can solve hard problems
LLMs, like Claude 3.5 'Sonnet', excel in tasks such as generating podcast transcripts, identifying speakers, and creating episode synopses efficiently. Their successful application demonstrates practicality and versatility in problem-solving.
4 commentsI thought it would be tragedy if this material were perpetually locked behind a paywall, so I worked hard to curate all the talks and make it freely available to everyone. I hope it is beneficial to everyone.
Related
GitHub – Karpathy/LLM101n: LLM101n: Let's Build a Storyteller
The GitHub repository "LLM101n: Let's build a Storyteller" offers a course on creating a Storyteller AI Large Language Model using Python, C, and CUDA. It caters to beginners, covering language modeling, deployment, programming, data types, deep learning, and neural nets. Additional chapters and appendices are available for further exploration.
LLMs on the Command Line
Simon Willison presented a Python command-line utility for accessing Large Language Models (LLMs) efficiently, supporting OpenAI models and plugins for various providers. The tool enables running prompts, managing conversations, accessing specific models like Claude 3, and logging interactions to a SQLite database. Willison highlighted using LLM for tasks like summarizing discussions and emphasized the importance of embeddings for semantic search, showcasing LLM's support for content similarity queries and extensibility through plugins and OpenAI API compatibility.
LLMs can solve hard problems
LLMs, like Claude 3.5 'Sonnet', excel in tasks such as generating podcast transcripts, identifying speakers, and creating episode synopses efficiently. Their successful application demonstrates practicality and versatility in problem-solving.