October 25, 2024
Summary A classic deep dive into Recurrent Neural Networks (RNNs) by Andrej Karpathy. This article brilliantly demonstrates how RNNs can learn and generate text, code, and even LaTeX math with remarkable coherence.
Key Takeaways RNNs can learn long-range dependencies in sequences Character-level models can generate surprisingly good text The model learns grammar, structure, and even code syntax Practical examples include Shakespeare, Wikipedia, Linux source code, and algebraic geometry papers Why I’m Sharing This Despite being from 2015, this remains one of the best introductions to understanding how neural networks process sequential data.
Read more →January 15, 2024
• 2 min read
Large Language Models (LLMs) have revolutionized artificial intelligence, powering applications from chatbots to code generation. In this article, we’ll explore the transformer architecture that makes LLMs possible, discuss popular models like GPT-4 and Claude, and examine real-world applications.
The Transformer Architecture The transformer architecture, introduced in the “Attention is All You Need” paper, forms the backbone of modern LLMs. Key components include:
Self-attention mechanisms that allow the model to weigh the importance of different words Multi-head attention for capturing different aspects of language Positional encoding to maintain sequence information Feed-forward networks for processing representations Popular LLM Models GPT-4 OpenAI’s GPT-4 represents a significant advancement in language understanding and generation, with improved reasoning capabilities and multimodal inputs.
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