A comprehensive guide to Large Language Models by Damien Benveniste, covering various aspects from transformer architectures to deploying LLMs.
- Language Models Before Transformers
- Attention Is All You Need: The Original Transformer Architecture
- A More Modern Approach To The Transformer Architecture
- Multi-modal Large Language Models
- Transformers Beyond Language Models
- Non-Transformer Language Models
- How LLMs Generate Text
- From Words To Tokens
- Training LLMs to Follow Instructions
- Scaling Model Training
- Fine-Tuning LLMs
- Deploying LLMs
- Language Models Before Transformers
- Attention Is All You Need: The Original Transformer Architecture
- A More Modern Approach To The Transformer Architecture
- Multi-modal Large Language Models
- Transformers Beyond Language Models
- Non-Transformer Language Models
- How LLMs Generate Text
- From Words To Tokens
- Training LLMs to Follow Instructions
- Scaling Model Training
- Fine-Tuning LLMs
- Deploying LLMs
- TabPFN is a novel foundation model designed for small- to medium-sized tabular datasets, with up to 10,000 samples and 500 features.
- It uses a transformer-based architecture and in-context learning (ICL) to outperform traditional gradient-boosted decision trees on these datasets.
- It uses a transformer-based architecture and in-context learning (ICL) to outperform traditional gradient-boosted decision trees on these datasets.
An article discussing ten predictions for the future of data science and artificial intelligence in 2025, covering topics such as AI agents, open-source models, safety, and governance.
Turn your Pandas data frame into a knowledge graph using LLMs. Learn how to build your own LLM graph-builder, implement LLMGraphTransformer by LangChain, and perform QA on your knowledge graph.
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This article features a curated list of the top data science articles published in July, covering topics such as LLM apps, chatGPT, data visualization, multi-agent AI systems, and essential data science skills for 2024.
An article discussing the current state, recent approaches, and future directions of prompt engineering in data and machine learning. It includes several links to relevant articles and tutorials on the topic.
An overview of the LIDA library, including how to get started, examples, and considerations going forward, with a focus on large language models (LLMs) and image generation models (IGMs) in data visualization and business intelligence.
The Towards Data Science team highlights recent articles on the rise of open-source LLMs, ethical considerations with chatbots, potential manipulation of LLM recommendations, and techniques for temperature scaling and re-ranking in generative AI.
