activity

newcomer

special subject

Open the CSDN application.

Copyright? CSDN.NET, 1999-2020, all rights reserved.

Search for blog posts/posts/users

log in

An old bird who likes soy sauce.

watch out for

Analysis on the Development Trend of Deep Learning Technology

20 19-04-09 08:37: 1 1

1 like it

An old bird who likes soy sauce.

Code age 2 years old

watch out for

/s/fthikinev 483 HIE 053 rpkg

At present, the development of artificial intelligence has been fully concerned and promoted by the breakthrough of deep learning technology. Governments around the world attach great importance to it, and the capital boom is still increasing. All walks of life have also reached a * * * understanding that it has become a hot spot for development. This paper aims to analyze the current situation of deep learning technology, judge the development trend of deep learning, and put forward development suggestions according to the technical level of our country.

First, the status quo of deep learning technology

Deep learning is the key technology of this round of artificial intelligence explosion. The breakthrough of artificial intelligence technology in computer vision and natural language processing ushered in a new round of explosive development of artificial intelligence. And deep learning is the key technology to achieve these breakthroughs. Among them, the image classification technology based on deep convolution network has exceeded the accuracy of human eyes, the speech recognition technology based on deep neural network has reached 95% accuracy, and the machine translation technology based on deep neural network has approached the average translation level of human beings. With the rapid improvement of accuracy, computer vision and natural language processing have entered the stage of industrialization and brought about the rise of emerging industries.

Deep learning is an algorithmic weapon in the era of big data and has become a research hotspot in recent years. Compared with the traditional machine learning algorithm, deep learning technology has two advantages. First, deep learning technology can continuously improve performance with the increase of data scale, while traditional machine learning algorithms can hardly use massive data to continuously improve performance. Second, deep learning technology can directly extract features from data, reducing the work of designing feature extractors for each problem, while traditional machine learning algorithms need to extract features manually. Therefore, deep learning has become a hot technology in the era of big data, and both academia and industry have done a lot of research and practical work on deep learning.

Various models of deep learning fully empower basic applications. Convolutional neural network and cyclic neural network are two widely used deep neural network models. Computer vision and natural language processing are two basic applications of artificial intelligence. Convolutional neural network is widely used in the field of computer vision, and its performance in image classification, target detection, semantic segmentation and other tasks greatly exceeds that of traditional methods. Cyclic neural network is suitable for solving problems related to sequence information, and has been widely used in the field of natural language processing, such as speech recognition, machine translation, dialogue system and so on.

Second, the development trend of deep learning

Deep neural network presents the development trend of deeper and deeper levels and more complex structure. In order to continuously improve the performance of deep neural network, the industry has been exploring from two aspects: network depth and network structure. The number of layers of neural network has expanded to hundreds or even thousands. With the deepening of network layers, its learning effect is getting better and better. In 20 15 years, the ResNet proposed by Microsoft exceeded the accuracy of image classification task for the first time with a network depth of 152 layers. New network design structures are constantly proposed, which makes the structure of neural network more and more complex. For example, 20 14, Google proposed the initial network structure, 20 15, Microsoft proposed the residual network structure, 20 16, and Huang Gao and others proposed the dense connection network structure. These network structure designs continuously improve the performance of the deep neural network.

The functions of deep neural network nodes are constantly enriched. In order to overcome the limitations of the current neural network, the industry has explored and proposed a new type of neural network node, which makes the functions of the neural network more and more abundant. 20 17, Jeffrey? Hinton put forward the concept of capsule network, which is closer to the behavior of human brain in theory by using capsules as network nodes to overcome the limitations of convolutional neural networks, such as lack of spatial stratification and reasoning ability. In 20 18, scholars from DeepMind, Google Brain and MIT jointly put forward the concept of graph network, and defined a new class of modules with relation induction bias function, aiming at giving deep learning the ability of causal reasoning.

Deep neural network engineering application technology is deepening. Most of the deep neural network models have hundreds of millions of parameters and occupy hundreds of megabytes of space, so it is difficult to deploy them to terminal devices with limited performance and resources such as smartphones, cameras and wearable devices. In order to solve this problem, the industry adopts model compression technology to reduce the parameters and size of the model and reduce the amount of calculation. At present, the model compression methods used include pruning the trained model (such as pruning, weight sharing and quantization). ) and design more elaborate models (such as MobileNet, etc.). ). The modeling and parameter adjustment process of deep learning algorithm is complex and the application threshold is high. In order to lower the application threshold of deep learning, the industry has put forward automatic machine learning (AutoML) technology, which can realize the automatic design of deep neural network and simplify the use process.