翻译：
What is the underlying logic of AI (artificial intelligence) – neural networks?
神经网络是人工智能技术的核心，是用相互连接的处理节点模拟生物神经、从大数据中学习模式并做出决策的计算模型，结构包含输入层、隐藏层与输出层，神经元负责加权求和，广泛用于语音、图像识别与自然语言处理，解决的是让机器从数据中学习模式并完成识别；它并不解决训练数据与算力成本高昂的问题。
1, Concept: A neural network is a computational model that uses multiple simple processing nodes (neurons) connected to each other to simulate a biological neural network. By learning large data sets, neural networks can recognize objects in images, analyze sentences, process audio, and so on, enabling various applications of artificial intelligence (AI) technology.
2, Structure: Neural network is composed of many layers of interconnected neurons, each layer of neurons contains a number of neural nodes, called node number. Neural networks usually include input layer, hidden layer and output layer.
(1) Input layer: The input layer accepts input data from external data sources, such as images, sounds, and text. For images, the input layer usually encodes the image in pixels, and for text, it usually encodes each word or character.
(2) Hidden layer: The hidden layer is the core part of the network, which contains a large number of neurons, each node represents a different feature of the network processing. The number and scale of hidden layers vary according to the complexity of the network and the characteristics of the task, and these hidden layers can work together to calculate some important features selected by the input layer. In neural networks, different hidden layers can perform different calculations, such as convolutions, loops, standard neural networks, etc.
(3) Output layer: The output layer is usually the last layer of the neural network, which summarizes the information of the hidden layer to form the result output. For example, in an image recognition problem, the output layer might be a label layer indicating a certain category to which the image belongs.
3. Neuron: Neuron is the basic unit of neural network. Each neuron has a set of inputs, calculates the weighted sum of those inputs, and converts that value to produce an output. The bias and weights of neurons need to be determined through data training.
In a neural network, each neuron is connected to other neurons, and a weight is set on each connection. The weights control how many signals each neuron is activated by, and control the strength of the output signal. The output of each neuron acts as the input of the next layer of neurons, thus allowing the neural network to calculate layer by layer and finally get the output.
In general, a neural network is a complex artificial intelligence (AI) computational model containing multiple neurons that can learn patterns and patterns from data and be used in a variety of applications. In the field of artificial intelligence (AI), neural networks are a very important technology, and the calculations and means it provides are very powerful. Carefully constructed neural networks are capable of processing large input data sets and producing the best results in their respective domains. Understanding the concept and structure of neural networks is key to building better artificial intelligence (AI) systems.
