This article demonstrates how to use the attention mechanism in a time series classification framework, specifically for classifying normal sine waves versus 'modified' (flattened) sine waves. It details the data generation, model implementation (using a bidirectional LSTM with attention), and results, achieving high accuracy.
>"I cover briefly what SVD is, and why it is central to making attention work really well."
This article details the release of Gemma 3, the latest iteration of Google’s open-weights language model. Key improvements include **vision-language capabilities** (using a tailored SigLIP encoder), **increased context length** (up to 128k tokens for larger models), and **architectural changes for improved memory efficiency** (5-to-1 interleaved attention and removal of softcapping). Gemma 3 demonstrates superior performance compared to Gemma 2 across benchmarks and offers models optimized for various use cases, including on-device applications with the 1B model.
This article provides a beginner-friendly explanation of attention mechanisms and transformer models, covering sequence-to-sequence modeling, the limitations of RNNs, the concept of attention, and how transformers address these limitations with self-attention and parallelization.
The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.
The article delves into how large language models (LLMs) store facts, focusing on the role of multi-layer perceptrons (MLPs) in this process. It explains the mechanics of MLPs, including matrix multiplication, bias addition, and the Rectified Linear Unit (ReLU) function, using the example of encoding the fact that Michael Jordan plays basketball. The article also discusses the concept of superposition, which allows models to store a vast number of features by utilizing nearly perpendicular directions in high-dimensional spaces.
The article explores the architectural changes that enable DeepSeek's models to perform well with fewer resources, focusing on Multi-Head Latent Attention (MLA). It discusses the evolution of attention mechanisms, from Bahdanau to Transformer's Multi-Head Attention (MHA), and introduces Grouped-Query Attention (GQA) as a solution to MHA's memory inefficiencies. The article highlights DeepSeek's competitive performance despite lower reported training costs.
The article provides a detailed exploration of DeepSeek’s innovative attention mechanism, highlighting its significance in achieving state-of-the-art performance in various benchmarks. It dispels common myths about the training costs associated with DeepSeek models and emphasizes its resource efficiency compared to other large language models.
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Perplexity AI's founder Aravind Srinivas outlines a vision where AI agents become the target audience for digital advertising, potentially replacing human attention.
