This repository on ML operations has free talks, books, papers and more: https://
bit.ly/MLops Developed by @visenger
MACHINE LEARNING
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Free MLops Repository with Talks Books and Papers
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DeepSeek’s 10X Efficiency Breakthrough Reshapes AI Resource Demands
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Take your intelligence estimates for Project Stargate and multiply them by 10. Every AI lab now has the means to make their models 10X+ more efficient because of DeekSeek. This doesn’t mean AI and chips will be used 10X less: the 10X cost decrease means it gets used 10X more.
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AI Model Efficiency Drives Bullish Outlook Innovation Wins
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We’re still in the first inning. A model that is 50x more efficient means… more reasons to be bullish on AI True that there is all to play for though – and the incumbents — or those with biggest CapEx— can’t rest easy. Innovation wins, and it is arrogant to think that it only
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Synthetic Data’s Role in AI Training and Data Privacy
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Ahead of #DataPrivacyDay, SAS' Brett Wujek explains the valuable link between the protection of personal data and synthetic data. Within just a few years, about half of the data used to train AI will be synthetic. This matters for you and your business. http://
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Visual Guide to Reinforcement Learning Concepts and Multi-Agent Training
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The best visual explanation of Reinforcement Learning (RL) I've ever seen! 😂
— Pascal Bornet (@pascal_bornet) 27 janvier 2025
It illustrates many key RL concepts like:
– Exploration and exploitation
– Imitation learning
– Reward mechanisms
– Distributed training
– Multi-agent cooperation
It's like watching multiple trials… pic.twitter.com/wNp4aGNdxuThe best visual explanation of Reinforcement Learning (RL) I've ever seen! It illustrates many key RL concepts like:
– Exploration and exploitation
– Imitation learning
– Reward mechanisms
– Distributed training
– Multi-agent cooperation It's like watching multiple trials -
AI Autonomy Reduces Human Involvement, Improves Results
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Less human involvement, more AI autonomy, better results.
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Supervised Fine-Tuning vs Reinforcement Learning: A Learning Analogy
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“Imagine teaching a child to ride a bike. You could give them a detailed manual (Supervised Fine Tuning), but they'll likely learn better by trying it themselves (Reinforcement Learning), falling, getting up, & gradually improving.” – @McDonaghMatthew ELI5 on DeepSeek, link
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Data Mix Specification Impact on Model Performance
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unclear tbh, data mix is not specified. probably is the big reason for performance
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China Launches Baichuan-M1 Deep Thinking Model Surpassing o1
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We released China's first full-scenario deep thinking model, Baichuan-M1-preview. It’s currently the only one in China that simultaneously possesses reasoning capabilities in 3 areas: language, vision, and search. It has surpassed o1-preview in multiple authoritative evaluations.