Why is deep learning better than traditional machine learning algorithms?

I. Data Dependency

The main difference between deep learning and traditional machine learning is that its performance grows as the size of the data increases. Deep learning algorithms do not perform well when there is very little data. This is because deep learning algorithms need a lot of data to understand it perfectly.

Three: Hardware Dependency

Deep learning algorithms require a lot of matrix operations, and GPUs are mainly used to optimize matrix operations efficiently, so GPUs are a must-have hardware for deep learning to work properly. Compared with traditional machine learning algorithms, deep learning relies more on high-end machines with GPUs.

Feature processing

Feature processing is the process of putting domain knowledge into a feature extractor to reduce the complexity of the data and generate patterns that make the learning algorithm work better. Feature processing is time-consuming and requires specialized knowledge.

Deep learning attempts to obtain high-level features directly from the data, which is the main difference between deep learning and traditional machine learning algorithms. Based on this, deep learning cuts down on the work of designing feature extractors for each problem.

For example, a convolutional neural network tries to learn low-level features in the previous layers, then learns a partial face, followed by a high-level description of the face. For more information you can read about interesting applications of neural network machines inside deep learning.

When applying traditional machine learning algorithms to solve a problem, traditional machine learning typically breaks the problem down into multiple sub-problems and solves them one by one before combining the results of all the sub-problems to get the final result. In contrast, deep learning promotes direct end-to-end problem solving.