As the popularity of artificial intelligence continues to rise, deep learning and machine learning have received sufficient attention as technologies for implementing AI. Both have had a huge impact in the fields of computer vision and natural language processing, and are also the key to driverless cars. technology.
What is deep learning?
In deep learning, computer learning performs tasks directly from images, text, or sound, while achieving higher accuracy, even more than human performance. Most deep learning algorithms use the neural network architecture, which is why deep learning models are often referred to as deep neural networks.
The so-called "depth" usually refers to the number of hidden layers in the neural network. The traditional neural network only contains 2-3 hidden layers, while the deep neural network can have up to 150. One of the most popular types of deep neural networks is the convolutional neural network (CNN or ConvNet), which convolves learning features by inputting data, and the 2D convolutional layer makes this architecture ideal for processing 2D data.
Taking a recognition picture as an example, a well-trained deep learning model can automatically identify objects in a picture, even though it has never seen these exact pictures before. In some websites, the specific characters in the uploaded photos can be recognized as deep learning. Today, many technologies applied in deep learning have been developed for more than ten years, and have matured results.
Why is deep learning more popular recently?
The main reason is accuracy, and the deep learning model can achieve unprecedented precision, sometimes even exceeding human performance. In addition, there are two other factors that have made the technology a concern for the industry:
深度 Deep learning requires a lot of tag data. For example, the development of driverless cars requires millions of images and thousands of hours of video, and these large amounts of tagged data are now readily available.
深度 Deep learning requires a lot of computing power. High-performance GPUs have an efficient deep learning parallel architecture that, when combined with clustering or cloud computing, can reduce training time for deep learning networks from weeks to hours or less.
Differences between machine learning and deep learning
Both deep learning and machine learning provide a way to train models and classify data. So what is the difference between the two?
Model extraction is different. Using machine learning algorithms, we need to manually select the relevant features of the image to train machine learning modeling. Then, the algorithm refers to these features when analyzing and classifying new objects. Reduce workflow because deep learning algorithms automatically extract relevant functionality from images. In addition, deep learning is an end-to-end learning, and the network is given tasks such as raw data and classification, which can be done automatically.
If you need to make a choice between deep learning and machine learning, you need to know whether you have a high-performance GPU and a lot of tagged data. Machine learning is more advantageous than deep learning if users don't have high-performance GPUs and tagged data. Because deep learning is often more complex, it may take thousands of images to get reliable results in terms of images. Only high-performance GPUs can help users and spend less time analyzing all the images in modeling.
If the user chooses machine learning, he can choose to train the model on a variety of different classifiers to know which functions can extract the best results. In addition, through machine learning, we can flexibly choose a combination of multiple ways to use Different classifiers and features to see which alignment is best for the data.
So, in general, deep learning is suitable for situations where the amount of computation is greater, while machine learning techniques are relatively easier to use.
Practical application of deep learning
Applications for deep learning applications cover industries such as autonomous driving, image recognition, and smart voice.
Autopilot: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is also used to detect pedestrians to reduce traffic accidents.
Industrial Automation: Deep learning helps people improve the safety of workers around heavy machinery by intelligently detecting when people or objects are within unsafe distances of the machine.
Human-computer interaction: Deep learning is used to automate auditory and speech translation, breaking through the barriers of human-computer communication.
Don't think that machine learning and deep learning are far away from you. As artificial intelligence gradually penetrates into all walks of life, computer vision, natural language processing, and robotics continue to innovate and evolve. These algorithms will appear in all aspects of life.
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