The definition of deep learning and a few historical elements
Deep learning is an ensemble of machine learning technics inspired by neural networks, which involves multiple layers of models. Deep learning is a buzzword (rebranding of neural network, although including now more layers) and covers a wide range of algorithms: Convolutional Neural Networks, Recurring Neural Networks, Long-Short Term Memory, are among the main ones.
The roots of deep learning are as old as the mid-20th century. The first algorithmic neural network, inspired from biology, is the 1957 Perceptron from Rosenblatt.
The first big step in the field happened in the 80s, when lastly a meaningful framework to optimize parameters was used in the neural network environment, namely gradient methods. At this stage the efficiency of these new methods was not proved at all.
One had finally to wait until the 2010s when a double revolution happened. This revolution was both linked to the volume of data (release of the ImageNet dataset) and to the increase of computational power (GPU Nvidia Cuda 1Trillion Operations per Seconds). Then deep learning started to win against any other methods in image processing and progressively also in the Natural Language Processing.
Then a revolution followed, with an incredible spreading-over in a very short time period, roughly 1.5 years.
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