Deep learning has become the technology of choice for most AI problems, making classic machine learning dwarf. However, despite the good performance of deep learning, classical machine learning methods still have some advantages, and in some specific cases it is better to use classical machine learning methods such as linear regression or decision trees instead of using a large deep network. This article compares deep learning and classic machine learning and introduces the advantages and disadvantages of the two technologies.
In recent years, deep learning has become the technology of choice for most AI problems, making classic machine learning dwarf. The reason is obvious. Deep learning shows excellent performance on many tasks such as speech, natural language, vision, and games. However, although deep learning has such good performance, classical machine learning methods still have some advantages, and in some special cases it is better to use classical machine learning methods, such as linear regression or decision trees, instead of using a large deep network.
This article will compare deep learning and classic machine learning, and introduce the advantages and disadvantages of the two technologies and what problems/how they are best used.
Deep learning is better than classic machine learning
First-rate performance: In many areas, deep networks have achieved far more accuracy than the classic ML methods, including speech, natural language, vision, games, and more. In many tasks, the classic ML method cannot even be compared with deep learning. For example, the following figure shows the image classification accuracy for different methods on the ImageNet dataset; blue for the classic ML method and red for the deep convolutional neural network (CNN) method. The classification error rate of deep learning methods is much lower than that of classical ML methods.
Efficient extensions with data: Compared to the classic ML algorithm, deeper networks can scale better if there is more data. The following figure is a simple example. In many cases, the best advice for improving accuracy with deep networks is to use more data! However, when using the classic ML algorithm, this fast and simple method has almost no effect and usually requires more complicated methods to improve the accuracy.
No feature engineering required: Classic ML algorithms usually require complex feature engineering. Typically, exploratory data analysis needs to be performed on the data set first. Then, you can reduce the dimensions to facilitate processing. Finally, the best features must be carefully selected to pass to the ML algorithm. When using deep learning, this feature engineering is not needed because only the data is passed directly to the network and usually good performance is immediately achieved. This completely eliminates the tedious and challenging feature engineering phase of the entire process.
Adaptable and easy to migrate: Compared with the classic ML algorithm, deep learning technology can adapt to different fields and applications more easily. First, migration learning can enable pre-trained deep networks to apply to different applications in the same area. For example, in computer vision, a pre-trained image classification network is often used as a feature extraction front end for target detection and segmentation networks. Using these pre-trained networks as front-ends can simplify the training of the entire model and often help to achieve higher performance in a shorter time.
In addition, the basic ideas and techniques for using deep learning in different fields are often shiftable. For example, once the basic theory of deep learning in the field of speech recognition is known, it is not difficult to learn how to apply a deep network to natural language processing because the basic knowledge required for both is very similar. But for the classic ML, this is not the case, because building a high-performance ML model requires domain-specific and application-specific ML techniques and feature engineering. For different fields and applications, the knowledge base of classic ML is very different, and often requires extensive specialized research in each separate field.
Classic machine learning is better than deep learning
Can work better on small data: In order to achieve high performance, deep learning requires a very large data set. The previously mentioned pre-trained network was trained on 1.2 million images. For many applications, such large data sets are not readily available, expensive, and time consuming. For smaller data sets, the classic ML algorithm is usually better than deep learning.
It's cheaper both financially and computationally: there is a lot of data, and it takes a reasonable amount of time to finish training. Deep learning requires the use of high-end GPUs. These GPUs are very expensive, but without them, it is difficult to implement high-performance deep networks. To use such a high-end GPU effectively, you also need a fast CPU, SSD storage, and fast and large-capacity RAM. The classic ML algorithm requires only a decent CPU to train well and does not require the best hardware. Because of their low computational cost, it is possible to iterate faster in a shorter period of time and try many different technologies.
Easier to explain: Since classic ML involves direct feature engineering, these algorithms are easy to interpret and understand. In addition, because we have a deeper understanding of the data and underlying algorithms, it is easier to adjust the parameters and change the model design. On the other hand, deep learning is a "black box". Even now, researchers cannot fully understand the "inside" of deep networks. Due to the lack of theoretical basis, hyperparameters and network design are also a great challenge.
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