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But that’s unironically a good idea so I decided to try and do it anyways. With the use of agents, I am now developing rustlearn (extreme placeholder name), a Rust crate that implements not only the fast implementations of the standard machine learning algorithms such as logistic regression and k-means clustering, but also includes the fast implementations of the algorithms above: the same three step pipeline I describe above still works even with the more simple algorithms to beat scikit-learn’s implementations. This crate can therefore receive Python bindings and even expand to the Web/JavaScript and beyond. This also gives me the oppertunity to add quality-of-life features to resolve grievances I’ve had to work around as a data scientist, such as model serialization and native integration with pandas/polars DataFrames. I hope this use case is considered to be more practical and complex than making a ball physics terminal app.
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AI確實存在一些非常現實的問題，從倫理問題到其可能對環境造成的影響。有些人甚至選擇完全不與它互動。但如果你打算使用大型語言模型，那麼學會如何更快、更有效率地獲得你想要的結果，不僅對你有好處，對過程中消耗的能源也可能更有益。以下這些技巧將幫助你入門。。关于这个话题，Safew下载提供了深入分析