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Total Size:
23.7 MB
Info Hash:
FF7790F2161C32964C67AC7EC3CA5E2EF63F211F
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Added:
April 20, 2026, 2:52 p.m.
Stats:
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(Last updated: April 20, 2026, 2:56 p.m.)
| File | Size |
|---|---|
| Trummer I. Data Analysis with LLMs. Text, tables, images and sound 2025.pdf | 23.7 MB |
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DL
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1.3 GB
[235
/
111]
2023-06-02
| Uploaded by Cristie | Size 1.3 GB | Health [ 235 /111 ] | Added 2023-06-02 |
NOTE
SOURCE: Trummer I. Data Analysis with LLMs. Text, tables, images and sound 2025
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COVER
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MEDIAINFO
Textbook in PDF format Speed up common data science tasks with AI assistants like ChatGPT and Large Language Models (LLMs) from Anthropic, Cohere, AI21, Hugging Face, and more! Using ChatGPT and other AI-powered tools, you can analyze almost any kind of data with just a few short lines of plain English. In Data Analysis with LLMs, you’ll learn important techniques for streamlining your data science practice, expanding your skillset and saving you hours—or even days—of time. Inside, you’ll learn how to use AI assistants to: Analyze text, tables, images, and audio files Extract information from multi-modal data lakes Classify, cluster, transform, and query multimodal data Build natural language query interfaces over structured data sources Use LangChain to build complex data analysis pipelines Prompt engineering and model configuration This practical book takes you from your first prompts through advanced techniques like building automated analysis pipelines and fine-tuning existing models. You’ll learn how to create meaningful reports, generate informative graphs, and much more. About the book: Data Analysis with LLMs teaches you to use a new generation of AI assistants and Large Language Models (LLMs) to simplify and accelerate common data science tasks. Cornell professor and long-time LLM advocate Immanuel Trummer reveals techniques he’s pioneered for getting the most out of LLMs in data science, including model selection and specialization, techniques for tuning parameters, and reliable prompt templates. You’ll start with an in-depth exploration of how LLMs work. Then, you’ll dive into no-code data analysis with LLMs, creating custom operators with the OpenAI Python API, and building complex data analysis pipelines with the cutting edge LangChain framework. About the reader: For data scientists, data analysts, and others who are interested in making their work easier through the use of artificial intelligence techniques. Readers should have a basic understanding of the Python programming language
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