Proficiency in Deep Learning and Transfer Learning

While traditional data analysis often involves simpler models, future ML engineers must understand deep learning architectures like CNNs, RNNs, and transformers. Transfer learning, which leverages pre-trained models for new tasks, is a powerful technique to reduce training time and data requirements—knowledge that aspiring engineers should actively acquire.

While traditional data analysis often involves simpler models, future ML engineers must understand deep learning architectures like CNNs, RNNs, and transformers. Transfer learning, which leverages pre-trained models for new tasks, is a powerful technique to reduce training time and data requirements—knowledge that aspiring engineers should actively acquire.

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