Deep Learning

Emerging technologies such as artificial intelligence (AI) are developing at an amazing speed and have made incredible progress in deep learning. Almost every developing technology branch benefits from the profound value of deep learning.

Deep learning is a part of a wider range of artificial machine learning family. It aims to imitate human behavior logic through artificial neural network. Its advantage is that it has the ability to investigate massive data sets and make complex decisions on massive data sets that human beings cannot achieve.

Deep learning has a model system similar to human brain, which can learn complex concepts. These systems can compare the new data with the benchmark data, so as to get effective learning and exercise. In order to improve the accuracy of these systems, more data must be provided to them to establish decision criteria for more complex data.

It is understandable that once this technology is commercially feasible, it is possible to penetrate into every industry. So far, according to the latest report of Market Research Future (MRFR), the value of deep learning market will reach US $17.4 billion by 2023. The application of deep learning and machine learning, big data and network security will open up a new environment for today's modern business. In the following sections, we will explore in depth how the machine learning branch of artificial intelligence promotes the development of emerging technologies.

Edge Calculation

Deep learning model can also play a role in edge calculation. Researchers have found that these systems can help machines identify various products and stimulate industrial automation. These systems can solve surface defects, identify products through their brightness and shape, and carry out complex inspection on site without manual intervention, so as to minimize human intervention. Machine vision uses the edge computing system to detect the quality of products and realize the artificial intelligence of manufacturing industry.

Artificial intelligence analysis

Artificial intelligence (AI) is another branch of artificial machine learning, which aims to design a self-conscious technical system that imitates human intelligence, rationality and personality. Artificial intelligence has evolved from a basic chat robot to a complex full-time assistant robot. Today, the most advanced AI system can quickly translate language and recognize network images with its tags. With this incredible development, enterprises and organizations are now using AI to solve some problems that cannot be solved by manual labor.

Big data expands the path of in-depth learning

Deep learning models traditionally rely on structured and unstructured data to establish decision-making processes. In speech recognition and text translation, the big data paired with this technology enables applications to build more complex speech recognition and text translation applications similar to human characteristics. The enhancement of label and graphics processing ability plays a key role in training deep learning model.

In addition, computer vision applications have also been developed through the pairing of big data and deep learning. It can make more human like decisions, thus bringing benefits to the development from military to medicine. These trends are likely to provide value in shipping, pharmaceuticals and other industries that rely on label and graphic design.

Enhance network security through in-depth learning

One of the main developments of network security is the application of deep insight. Deep insight has developed a mobile and endpoint network security solution to use deep learning to detect real-time threats between servers, endpoints and mobile phones. This technology, which enables deep learning, can prevent attacks and predict unknown attacks through deep learning algorithms, distinguish harmful attacks from harmless attacks, and immediately extend its protection to the whole network. This process requires the virtualization of the network, or the combination of virtual machines and containers, to maximize the allocation of resources and isolate services for faster computing. In order to improve the speed of edge computing, we need to solve the problems of privacy, risk control and response delay.

Future development

With the progress of technology, whether it is AI, network security or big data, as deep learning continues to promote the technological innovation of the industry and the development of emerging industries, we will see more amazing progress.