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Synthetic Data Quality Limitations Compared to Raw Data
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yes, totally. But my guess is model-generated synthetic data will always be a bit inferior then raw data (assuming raw data doesn't contain too much false information).
Because in the best case synthetic data preserves the information & in the worst case it adds hallucinations -
Large Concept Models: New AI Frontier Beyond Token-Level Language
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Check out the latest article in my newsletter: Large Concept Models (LCM): A New Frontier in AI Beyond Token-Level Language Models https://
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Deep Reinforcement Learning Hands-On Guide Third Edition
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Deep #ReinforcementLearning Hands-On — A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF: http://
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Artificial Intelligence Methods for Cybersecurity Solutions and Design
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Artificial Intelligence for #Cybersecurity: https://
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Convex Optimization: Best-Seller Guide to Numerical Solutions
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Convex Optimization (#1 best-seller in Linear Programming): https://
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Enterprise Machine Learning Systems: MLOps Requirements and Abacus AI Solutions
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Synthetic Data for Pretraining: Proof-of-Concept Study
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True, but I think it's more of a proof-of-concept / study of synthetic data for pretraining rather than generating a production-ready model.
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Synthetic Data Structure Benefits and Knowledge Limitations
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Yeah, I think the main advantage is from the more refined nature of the synthetic data when it comes to response structure etc. Synthetic data can't really contain knowledge that raw data doesn't already include because the raw data was used to come up with the synthetic data in