大トロ ・ Machine Learning

Unless you've been living under a rock, you would've noticed that artificial neural networks are now used everywhere. They're impacting our everyday lives, from performing predictive tasks such as recommendations, facial recognition and object classification, to generative tasks such as machine translation and image, sound, video generation. But with all of these advances, the impressive feats in deep learning required a substantial amount of sophisticated engineering effort. Even if we look at the early AlexNet from 2012, which made deep learning famous when it won the ImageNet competition back then, we can see the careful engineering decisions that were involved in its design. Modern networks are often even more sophisticated, and require a pipeline that spans network architecture and careful training schemes.