Interview
summary
Argues that deep learning does not require lots of data, many GPUs, or years of math education and shows practical steps to make it accessible. Contrasts classic tools like random forests and gradient boosting with modern convolutional and NLP models and points to concrete wins on image tasks and emerging gains in language. Claims reproducible workflows and teaching materials exist that let practitioners get useful deep models without enterprise-scale infrastructure.
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