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The biggest difference between deep learning and machine learning is "performance"; machine learning is mainly used to make machines have intelligence, but deep learning is a technology to implement machine learning, and deep learning is also a kind of machine learning.

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What is the difference between learning and machine learning?

The biggest difference between deep learning and machine learning is performance.

Machine learning is mainly used to make machines have intelligence, but deep learning is a technology to achieve machine learning, and deep learning is also a kind of machine learning. If the amount of data is relatively small, the performance of deep learning is relatively poor, because deep learning algorithms must have a large amount of data to understand the patterns well.

Generally speaking, artificial intelligence is more topical, but it is now well known to people in the field of using artificial intelligence, and it has also had a great impact on these fields. Because of the importance of using artificial intelligence, systems have been developed that can not only simulate human thought processes, but also learn knowledge from processed data, and this phenomenon is machine learning.

1. Data dependency, the main difference between deep learning and machine learning is performance. Deep learning doesn't perform well when the amount of data is small, because deep learning algorithms need a lot of data to understand the underlying patterns.

2. Hardware support, deep learning algorithms rely heavily on high-end machines, while traditional machine learning algorithms can run on low-end machines. Deep learning requires GPUs to do a lot of matrix multiplication.

3. Feature engineering, feature engineering is to input domain knowledge into feature extractor to reduce data complexity. This process is expensive in terms of time and expertise.

4. Solutions, usually, we use traditional algorithms to solve problems. This requires breaking the problem into parts, solving them separately, and combining them after obtaining the results.

5. Execution time, because deep learning contains many parameters, it will take more time than machine learning. Machine learning takes less time to train data and takes only seconds to hours.

The main application scenarios are:

Computer vision: license plate recognition, Face Recognition.

Information retrieval: search engine, text retrieval, image retrieval.

Marketing: automated email marketing, target recognition.

Medical diagnosis: cancer detection, abnormality detection.

Natural language processing: semantic analysis, photo tagging, online advertising.

From the perspective, it is mainly:

1. Machine learning and data science are gaining momentum, and the use of machine learning in business is becoming increasingly important for businesses that want to survive.

2. Deep learning has proven to be one of the most advanced technologies in the art, and it has brought an infinite number of surprises to people, and it is believed that the future will also be the same.

3. Researchers are still exploring machine learning and deep learning. In the past, the research on them was confined to academic scope, but now the industrial circles have also intensified their research efforts.

The best proof is image recognition, which is increasingly becoming an AI-led field. The system can be designed to manipulate pre-written routines that analyze shapes, colors, and objects in pictures, scanning millions of images in order to teach itself how to recognize images correctly.

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