What are neural networks, deep learning, and machine learning? What are the differences and connections?

Deep learning is a word made from deep neural networks + machine learning. Deep first appeared in DEEP BELIEF NETWORK (deep (layer) belief network). The emergence of deep belief networks has made neural networks, which have been dormant for many years, rejuvenated. GPUs have made it possible to train deep networks with random initialization, and the emergence of resnet has broken the spell of hierarchical limitation, making it possible to train deeper neural networks.

Deep learning is the only development and continuation of neural networks. In the current language, deep learning is generalized to neural networks, and neural networks are generalized to deep learning.

There is no difference in the current context.

Definition

Biological neural network mainly refers to the neural network of the human brain, which is the technical prototype of artificial neural network. The human brain is the material basis of human thinking, and the function of thinking is localized in the cerebral cortex, the latter of which contains about 10^11 neurons, each of which is in turn connected to about 103 other neurons through synapses, forming a highly complex and highly flexible dynamic network.

As a discipline, Biological Neural Networks focuses on the study of the structure and function of the neural network of the human brain and its working mechanism, with the intention of exploring the laws of human brain thinking and intelligent activities.

Artificial neural network is a technical reproduction of biological neural network in a simplified sense, as a discipline, its main task is to build a practical artificial neural network model according to the principle of biological neural network and the needs of practical applications, design corresponding learning algorithms, simulate a certain kind of intelligent activity of the human brain, and then technically realize it to solve the practical problems.

So, biological neural networks mainly study the mechanism of intelligence; artificial neural networks mainly study the realization of the mechanism of intelligence, and the two complement each other.