Deep Learning, also known as Deep Neural Networks, is a perceptron. Born in the Dartmouth conference the following year, Ta is destined to have a close relationship with artificial intelligence. Various machine learning methods in artificial intelligence, from early sign learning to later statistical learning to deeper learning now, often represent school disputes. How does the perceptive machine that arrived at the first time dare to compete with the dominant hegemony of the time, symbolism? Because ta has a cry 'connectionism'. The history of the two factions tit-for-tat is not shown here. Let's take a look at this newborn calf and perceptive machine, and how to gradually grow into the profound learning of today. Perceptron, a mysterious name, what is it? Although borrowed from the metaphor of human brain neurons, it is ultimately a linear classifier with two layers of input and output neurons. However, the world is not always linear. How can a linear classifier that can't be solved by XOR entrust it? One of the fathers of artificial intelligence, Marvin Minsky, even criticized it with a book called Perception. It was this book that almost killed deep learning. As soon as the big brother made a speech, various people and horses rushed to hate each other, and forgot about the rivers and lakes. The neural network entered this winter period. Figure 1. The Dartmouth meeting reunite for fifty years. The middle man is Minsky, who died on January 24, 2016. Most people gave up, but some people insisted on it. In order to solve the nonlinear separability problem, humans (Rumelhart, Williams, Hinton, LeCun, etc.) added some hidden layers to the perceptron, so the 'multilayer perceptron' was born, and each layer of neurons was born. The neural network structure connected only to the lower layers and connected at different layers between neurons is a basic neural network, 'feedforward networks'. How to train this giant was still unclear at the time. Until the mid-1980s, the emergence of the BP algorithm rekindled the hope of connectionism. The BP algorithm provides a simple and elegant calculus solution for training multi-layer networks, making the neural network a realistic and usable model. This is the second wave of neural networks. It is not difficult to imagine that although the model can fit more and more complex functions with increasing neural network layers, how to avoid local optimal solutions and how to avoid gradient disappearance is still not effective. The theory of statistical learning was also introduced into the room during this period, and there was a great deal of success. The neural network was once again thrown into the cold. Until the arrival of the new millennium. With the dramatic increase in computing power and the emergence of big data, and the emergence of training methods such as ReLU and pre-training, the neural network recreated the name of 'deep learning' and swept the major pattern recognition contests in 2012. No further failure so far. Internet giants see the situation is good, they have also been put into battle, invested heavily, deep learning began to rush all the way to the pinnacle of life. Interestingly, the history of the rise and fall of neural networks happens to be a change in the name and surname history - from 'Perception Machine' to 'Neural Network' to the 'Deep Learning' that is hot nowadays. The importance of the name. If we use five words to summarize the revival of this deep learning, I think it is probably - the new bottle of old wine, wrong, it should be, the time to build a hero. The wheels of artificial intelligence roll forward, thirty years of Hedong, thirty years of Hexi. This connectionism has taken the limelight. Who will be the next time? Figure 2. Famous Pattern Recognition Contest ImageNet: Large Scale Visual RecogniTIon Challenge
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