翻译：
What is deep learning, the underlying logic of AI (artificial intelligence)?
深度学习（Deep Learning）是机器学习的一个分支，核心是把大规模数据送入多层神经网络逐层提取特征并自动学习规律，主要由前馈、循环（RNN）与卷积（CNN）三类网络构成，解决的是图像、语音、文本识别的准确率问题，并不解决训练数据偏差造成的结论偏差问题。
1. Concept: Deep learning is a combination of neural networks whose core idea is to transfer large data sets into multi-layer neural networks for training. With each layer of processing, the network is able to capture more features and continuously improve accuracy, and eventually can correctly identify data such as images, speech, text, and find its underlying patterns.
2. Implementation method: The implementation of deep learning is usually based on three types of neural networks: feedforward neural network, recurrent neural network and convolutional neural network. Each neural network has different structures and applications, but together they utilize multiple neurons and process input data at multiple levels.
(1) Feedforward neural network: Feedforward neural network is the simplest kind of neural network, with multiple internal nodes and layers. Data flows into the network from the input layer like water flow, and is transmitted in several layers, weighted and reached the output layer to achieve classification, regression and other operations.
Recurrent neural network (RNN) is a type of neural network that combines past information with present input and is widely used in fields such as natural language processing and speech recognition. Recurrent neural networks have additional “memory units” that can be used to store previous states of the network and allow the network to go back to previous states to learn. In this way, the network can better handle sequential data, such as time series, natural language and audio data.
Convolutional Neural networks: Convolutional neural networks (CNNS) are deep learning architectures that aim to extract meaningful features from data such as images, video, and audio. Due to the design of the convolutional layer, CNNS are particularly suitable for data processing containing spatial information. In the convolution layer, different weights are convolved to extract different features (such as edges, corners, textures, etc.).
Overall, deep learning is a powerful machine learning technique that can find patterns in large data sets and generate accurate predictions. The implementation of deep learning is mainly based on feedforward, loop and convolutional neural networks. These neural network models are multi-layered and can learn and process input data with different spatial, temporal, or sequential properties.
