The difference between deep learning and machine learning

The differences between deep learning and machine learning are as follows:

1, data volume

Machine learning can adapt to all kinds of data, especially scenes with small data. On the other hand, if the amount of data increases rapidly, the effect of deep learning will be more prominent. The following figure shows the efficiency levels of machine learning and deep learning under different data volumes.

2. Hardware dependency

Contrary to the traditional machine learning algorithm, deep learning algorithm is highly dependent on high-end equipment in design. Deep learning algorithm needs a lot of matrix multiplication, so it needs enough hardware resources as support.

3. Characteristic engineering

Feature engineering is a process of putting knowledge in a specific domain into specific features, aiming at reducing the complexity level of data and generating patterns that can be used for learning algorithms.

4. Solution to the problem

Traditional machine learning algorithms follow standard procedures to solve problems. It divides the problem into several parts, solves them separately, and then combines the results to get the required answer. Deep learning solves problems in a centralized way without problem splitting.

5. Execution time

Execution time refers to the time needed to train the algorithm. Deep learning needs a lot of time to train, because it contains more parameters, so the training time is more considerable. Relatively speaking, the execution time of machine learning algorithm is relatively short.

6. Interpretability

Interpretability is one of the main differences between machine learning and deep learning algorithms-deep learning algorithms often do not have interpretability. It is precisely because of this that the industry always thinks twice before using deep learning.