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Reinforcement Learning, Part 5: Temporal-Difference Learning | by Vyacheslav Efimov | Jul, 2024

Intelligently synergizing dynamic programming and Monte Carlo algorithms R einforcement learning is a domain in machine learning that introduces the concept of an agent learning optimal strategies in complex environments. The agent learns from its actions, which result in rewards, based on the environment’s state. Reinforcement learning is a challenging topic and differs significantly from…

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AI-Proof Your Data Science Skill Set by Embracing Four Timeless Concepts | by Matthew Gazzano | Jul, 2024

And stay competitive in a saturated job market Photo by Thomas Kelley on UnsplashWith AI productivity tools like Microsoft Copilot, ChatGPT, and many others emerging, some technology professionals have drawn concerns around their skill sets becoming obsolete. Since AI is still in its infancy, it’s impossible for anyone to predict exactly how the skill sets…

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Forget Statistical Tests: A/B Testing Is All About Simulations | by Samuele Mazzanti | Jul, 2024

How simulations outperform traditional stats in that they are easier to understand, more flexible, and economically meaningful [Image by Author]Controlled experiments such as A/B tests are used heavily by companies. However, many people are repelled by A/B testing due to the presence of intimidating statistical jargon including terms such as “confidence”, “power”, “p-value”, “t-test”, “effect…

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