AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? | IBM

摘要: Deep Learning就像最新式的手機/電腦CPU，遊戲等等，好的很。可CPU早就有了，超級瑪麗、魂鬥羅、紅警這些遊戲也早就有了。

Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls [套娃]. Each is essentially a component of the prior term.

That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

The "deep" in deep learning is referring to the depth of layers in a neural network. A neural network that consists of more than three layers - which would be inclusive of the inputs and the output can be considered a deep learning algorithm. 很像不同的i3基本上只有主頻的差異，核數夠多就升檔為i5, i7, i9了。

Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required.

It also enables the use of large data sets, earning itself the title of "scalable machine learning" in this MIT lecture.

Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

The future of deep learning, according to its pioneers – TechTalks (bdtechtalks.com)