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• Supervised learning, in which the machine is presented with input data
and a desired output, and the goal is to learn from those training examples
in such a way that meaningful predictions can be made for data that the
machine has never observed before.
• Unsupervised learning, in which the machine is presented with input data
only, and the machine has to subsequently find some meaningful structure
by itself, with no external supervision or input.
Preface
• Reinforcement learning, in which the machine acts as an agent, interacting
with the environment. The machine is provided with "rewards" for behaving
in a desired manner, and "penalties" for behaving in an undesired manner.
The machine attempts to maximize rewards by learning to develop its
behavior accordingly.
DL took the world by storm in 2012. During that year, the ImageNet 2012 challenge
[3] was launched with the goal of predicting the content of photographs using a
subset of a large hand-labeled dataset. A deep learning model named AlexNet
[4] achieved a top-5 error rate of 15.3%, a significant improvement with respect to
previous state-of-the-art results. According to the Economist [5], "Suddenly people
started to pay attention, not just within the AI community but across the technology
industry as a whole." Since 2012, we have seen constant progress [5] (see Figure 1)
with several models classifying ImageNet photography, with an error rate of less
than 2%; better than the estimated human error rate at 5.1%:
Figure 1: Top 5 accuracy achieved by different deep learning models on ImageNet 2012
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