In this episode, we break down the science behind Adam, a powerful algorithm revolutionizing how machines learn. Designed for efficiency and flexibility, Adam handles noisy, sparse data and large-scale problems with ease. We'll explore how it adapts to shifting objectives, why it needs minimal tuning, and what makes it stand out from other optimization methods. Plus, we’ll touch on its sibling, AdaMax, and the clever math that makes these tools a favorite in AI research and applications. Whether you’re an expert or just curious, we’ll keep it simple and engaging!

Link to research paper- https://arxiv.org/pdf/1412.6980

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