Impact of deep learning
To show you some of the impacts of deep learning, let’s take a look at two specific areas: image recognition and speed recognition.
The following figure, Performance on ImageNet classification over time, shows the top five error rate trends for ILSVRC contest winners over the past several years. Traditional image recognition approaches employ hand-crafted computer vision classifiers trained on a number of instances of each object class, for example, SIFT + Fisher vector. In 2012, deep learning entered this competition. Alex Krizhevsky and Professor Hinton from Toronto university stunned the field with around 10% drop in the error rate by their deep convolutional neural network (AlexNet). Since then, the leaderboard ...
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