The highlighted articles cover a variety of topics, including algorithmic thinking for data scientists, outlier detection in time-series data, route optimization for visiting NFL teams, minimum vertex coloring problem solution, high-cardinality features, multilingual RAG (Rapidly-explainable AI) system development, fine-tuning smaller transformer models, long-form visual understanding, multimodal image-text models, the theoretical underpinnings of learning, data science stress management, and reinforcement learning.
Generate instruction datasets for fine-tuning Large Language Models (LLMs) using lightweight libraries and documents.
Distilling key points after >2 years of experience and from AI developers’ own tutorials, hands-on and with examples.
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