深度学习与传统的人工神经网络机器学习之间有什么区别? [英] What is the difference between Deep Learning and traditional Artificial Neural Network machine learning?
问题描述
您能否简要说明一下深度学习与传统机器学习之间的区别，神经网络?使神经网络变深"需要多少级?这仅仅是营销炒作吗?
Can you offer a concise explanation of the differences between Deep Learning and Traditional Machine Learning that utilize neural networks? How many levels are need to make a neural network "deep"? Is this all just marketing hype?
推荐答案
我希望与@Frank Puffer的答案有所不同.我不明白他在隐藏层等上执行无监督学习程序的意思.
I beg to differ with @Frank Puffer's answer. I don't understand what he meant by performing an unsupervised learning procedure on the hidden layers etc.
深度学习是指通常具有2或3个以上的隐藏层的神经网络模型.大多数DL模型具有10到100或更多的层.
Deep Learning refers to Neural Network models with generally more than 2 or 3 hidden layers. Most DL models have 10 to 100 or more layers.
深度学习模型的最新革命取决于两件事:
1.大量数据的可用性-这是互联网时代的产物
2. GPU的可用性
The recent revolution in the Deep Learning models relies on two things:
1. the availability of lots of data--which is a product of the internet age
2. the availability of GPUs
用于DL模型优化的算法称为反向传播算法(在数学上等效于体面梯度).实际上，至少从80年代开始，反向传播技术就已经存在了-这不是DL专有的东西.
The algorithm used for optimization of DL models is called the backpropagation algorithm (which is mathematically equivalent to gradient decent). Backprop actually has been around since at least the 80s--it's not a DL specific thing.
由于模型的复杂性和规模，DL模型通常需要大量的数据.它们通常具有数百万个可调重量参数.由于训练数据的大小以及每次迭代需要计算的数百万个偏导数(相对于权重)，因此优化需要很高的计算能力.
DL models generally require copious amounts of data due to the complexity and size of the models. They typically have millions of tunable weight parameters. The optimization requires high compute power because of the size of training data and the millions of partial derivatives (with respect to the weights) that need to be computed at each iteration.
从本质上讲，深度学习不是营销炒作.这是一个大型的多层神经网络模型，需要大量数据和强大的GPU进行训练.并且经过培训，他们可以在某些任务上实现超人的准确性.
In essence, Deep Learning is not a marketing hype. It's a large multi layered Neural Network model that requires lots of data and powerful GPUs to train. And once it's trained they achieve super-human accuracies at certain tasks.
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