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Evolutionary Powell's method is a discrete optimization algorithm I've found useful for hyperparameter tuning.

It makes weaker assumptions than Bayesian methods (and so is more robust), but stronger than random exploration (and so has better performance). It fills in the gap between then a bit.

Here's the full post on how Evolutionary Powell's method works:

We develop it as part of End-to-End Machine Learning Course 314:

The open source Ponderosa optimization package where it lives:

The line-by-line code walkthrough:

❇️ @AI_Python_EN



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Evolutionary Powell's method is a discrete optimization algorithm I've found useful for hyperparameter tuning.

It makes weaker assumptions than Bayesian methods (and so is more robust), but stronger than random exploration (and so has better performance). It fills in the gap between then a bit.

Here's the full post on how Evolutionary Powell's method works:

We develop it as part of End-to-End Machine Learning Course 314:

The open source Ponderosa optimization package where it lives:

The line-by-line code walkthrough:

❇️ @AI_Python_EN

BY AI, Python, Cognitive Neuroscience


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The lead from Wall Street offers little clarity as the major averages opened lower on Friday and then bounced back and forth across the unchanged line, finally finishing mixed and little changed.The Dow added 33.18 points or 0.10 percent to finish at 34,798.00, while the NASDAQ eased 4.54 points or 0.03 percent to close at 15,047.70 and the S&P 500 rose 6.50 points or 0.15 percent to end at 4,455.48. For the week, the Dow rose 0.6 percent, the NASDAQ added 0.1 percent and the S&P gained 0.5 percent.The lackluster performance on Wall Street came on uncertainty about the outlook for the markets following recent volatility.

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