머신러닝과 딥러닝 | 딥러닝 입문
해당 시리즈: Introduction to Deep Learning
*한국어 자막을 포함한 영상입니다.
이 영상에서는 머신러닝과 딥러닝의 차이점을 비교하고, 어떤 상황에 어느 기술을 사용해야 할지 결정하는 데 도움을 줍니다. 머신러닝에서는 이미지의 특징을 수동으로 선택해 모델을 훈련시키는 반면, 딥러닝은 이미지를 직접 알고리즘에 입력해 객체를 예측합니다. 고성능 GPU와 충분한 레이블 데이터의 유무는 머신러닝과 딥러닝 선택에 중요한 요소로, MATLAB을 사용하면 다양한 분류기와 특징의 조합을 빠르게 시도해 볼 수 있습니다.
머신러닝과 딥러닝 사이의 선택은 사용할 데이터와 해결하려는 문제에 따라 달라지며, MATLAB은 이 두 기술을 별도로 또는 결합된 접근 방식으로 사용하는 데 도움을 줍니다.
녹화된 날짜: 2017년 3월 24일
Deep learning and machine learning both offer ways to train models and classify data. This video compares the two, and it offers ways to help you decide which one to use. Let's start by discussing the classic example of cats versus dogs. Now, in this picture, do you see a cat or a dog? How are you able to answer that? Chances are you've seen many cats and dogs over time, and so you've learned how to identify them. This is essentially what we're trying to get a computer to do: learn from and recognize examples.
Also keep in mind that sometimes even humans can get identification wrong, so we might expect a computer to make similar errors. To have a computer do classification using a standard machine learning approach, we'd manually select the relevant features of an image, such as edges or corners, in order to train the machine learning model. The model then references those features when analyzing and classifying new objects.
This is an example of object recognition. However, these techniques can also be used for scene recognition and object detection. When solving a machine learning problem, you follow a specific workflow. You start with an image, and then you extract relevant features from it. Then you create a model that describes or predicts the object. On the other hand, with deep learning, you skip the manual step of extracting features from images. Instead, you feed images directly into the deep learning algorithm, which then predicts the object.
So deep learning is a subtype of machine learning. It deals directly with images and is often more complex. For the rest of the video, when I mention machine learning, I mean anything not in the deep learning category. When choosing between machine learning and deep learning, you should ask yourself whether you have a high-performance GPU and lots of labeled data.
If you don't have either of these things, you'll have better luck using machine learning over deep learning. This is because deep learning is generally more complex, so you'll need at least a few thousand images to get reliable results. You'll also need a high-performance GPU so the model spends less time analyzing those images. If you choose machine learning, you have the option to train your model on many different classifiers. You may also know which features to extract that will produce the best results.
Plus, with machine learning, you have the flexibility to choose a combination of approaches. Use different classifiers and features to see which arrangement works best for your data. You can use MATLAB to try these combinations quickly. Also keep in mind that if you are looking to do things like face detection, you can use out-of-the-box MATLAB examples.
As we mentioned before, you need less data with machine learning than with deep learning, and you can get to a trained model faster too. However, deep learning has become very popular recently because it is highly accurate. You don't have to understand which features are the best representation of the object. These are learned for you. But in a deep learning model, you need a large amount of data, which means the model can take a long time to train.
You are also responsible for many of the parameters, and because the model is a black box, if something isn't working correctly, it may be hard to debug.
So, in summary, the choice between machine learning and deep learning depends on your data and the problem you're trying to solve. MATLAB can help you with both of these techniques, either separately or as a combined approach. To find out more, visit mathworks.com/deep-learning.
웹사이트 선택
번역된 콘텐츠를 보고 지역별 이벤트와 혜택을 살펴보려면 웹사이트를 선택하십시오. 현재 계신 지역에 따라 다음 웹사이트를 권장합니다:
또한 다음 목록에서 웹사이트를 선택하실 수도 있습니다.
사이트 성능 최적화 방법
최고의 사이트 성능을 위해 중국 사이트(중국어 또는 영어)를 선택하십시오. 현재 계신 지역에서는 다른 국가의 MathWorks 사이트 방문이 최적화되지 않았습니다.
미주
- América Latina (Español)
- Canada (English)
- United States (English)
유럽
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
아시아 태평양
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)