Hands-On Deep Learning with Go

DL – hype or breakthrough?

DL and the associated hype is a relatively recent development. Most discussion of its emergence centers around the ImageNet benchmarks of 2012, where a deep convolutional neural network beat the previous year's error rate by 9%, a significant improvement where previous winners had made incremental improvements at best with techniques that used hand-crafted features in their models. The following diagram shows this improvement:

Despite the recent hype, the components that make DL work, which allow us to train deep models, have proven very effective in image classification and various other tasks. These were developed ...