머신러닝과 딥러닝: 주요 차이점 설명

머신러닝은 레이블이 지정된 데이터에 의존하는 반면, 딥러닝은 비정형 데이터의 패턴을 자율적으로 식별합니다.

### 피자를 통한 머신 러닝과 딥 러닝의 이해
* 🍕 "지금 배가 고프다면 **이 동영상을 잠시 멈추고 간식을 드시고** 계속하세요."
* 🤖 딥러닝은 머신러닝의 하위 집합으로, 그 자체가 인공지능(AI)의 하위 분야이며, **딥러닝 알고리즘(DL)**의 중추로서 **신경망(NN)**과 계층 구조를 형성합니다.
* 📊 머신 러닝 알고리즘은 세 가지 요소를 기반으로 피자 주문 여부를 결정하는 모델에서 볼 수 있듯이 구조화된 레이블이 지정된 데이터를 예측에 활용합니다: **시간 절약(X1)**, 체중 감량(X2)**, 비용(X3)**.
* ⚖ 각 입력에는 가중치가 할당되며 가중치가 클수록 **더 큰 중요도**를 나타내며, 이 경우 가중치는 시간 **5, 체중 감량 **3, 비용 **2입니다.
* 📈 이 모델은 임계값에 대한 가중치 입력을 합산하여 출력을 계산하며, 합계가 **+2**인 경우 피자의 밤에 대한 **긍정적 결정**으로 이어집니다.
* 🔍 반면, 딥러닝은 **3개 이상의 계층**을 가진 신경망을 사용하여 비정형 데이터의 특징을 식별하는 데 **인간의 개입이 거의 필요 없으며, 레이블이 지정된 데이터 세트 없이 이미지와 같은 원시 데이터로부터 **학습**할 수 있습니다.
* 👥 기존 머신러닝은 지도 학습을 위해 **인간 전문가에게 의존하여 특징을 정의**하는 반면, 딥러닝은 비지도 학습 시나리오에서 **자동으로 패턴을 발견**할 수 있습니다.
* 🧠 궁극적으로 머신러닝과 딥러닝은 모두 AI 내에서 서로 연결된 분야로, 주로 신경망의 복잡성**과 데이터 라벨링에서 사람의 입력이 필요하다는 점에서 차별화됩니다.

Look, fair warning, if you're feeling a little hungry right now, you might want to pause this video and grab a snack before continuing. Because I'm going to explain the difference between machine learning and deep learning by talking about pizza. Delicious, tasty pizza. Now, before we get to that, let's address the fundamental question here. What is the difference between these two terms? Well, put simply, deep learning is a subset of machine learning. Actually, the hierarchy goes like this. At the top, we have AI or artificial intelligence. A subfield of AI is ML or machine learning. Beneath that, then we have NN or neural networks. And they make up the backbone of deep learning algorithms, DL. And here on the IBM technology channel, we have a whole bunch of videos on these topics you might want to consider subscribing. Now, machine learning algorithms leverage structured labeled data to make predictions. So let's build one a model to determine whether we should order pizza for dinner.

There are three main factors that influence that decision. So let's map those out as inputs. The first of those inputs will call X1. And X1 asks, Will it save time by ordering out? We can say yes with a 1 or no with a 0. Yes, it will - so X1 equals 1. Now X2. That input says, Will I lose weight by ordering pizza? That's a 0. I'm ordering all the toppings. And X3... Will it save me money? Actually, I have a coupon for a free pizza today, so that's a 1. Now look, these binary responses, ones and zeros. I'm using them for simplicity. But neurons in a network can represent values from, well, everything to everything. Negative infinity to positive infinity. With our inputs defined, we can assign weights to determine importance. Larger weights make a single input's contribution to the output more significant compared to other inputs. Now my threshold here is 5, so let's weight each one of these, W1.

Well, I'm going to give this a full 5 because I value my time. W2, This was the will I lose weight one. I'm going to write this a 3 because I have some interest in keeping in shape, and for W3, I'm going to give this a 2 because either way, this isn't going to break the bank to order dinner. Now we put these weights into our model, and using an activation function, we can calculate the output, which in this case is the decision to order pizza or not. So to calculate that, we're going to calculate the why hat. And we're going to use these weights and these inputs. So here we've got 1x5. We've got 0x3. And we've got 1x2. And we need to consider as well our threshold, which was 5. So that gives us if we just add these up 1x5, that's 5 - plus 0x3, that's zero plus 1x2, that's 2 - minus 5.

Well, that gives us a total of +2. And because the output is a positive number, this correlates to pizza night! OK, so that's machine learning. But what differentiates deep learning? Well, the answer to that is more than 3. As in, a neural network is considered a deep neural network if it consists of more than three layers, and that includes the input and the output layer. So, we've got our input and output, we have multiple layers in the middle. And this would be considered a deep learning network. Classical machine learning is more dependent on human intervention to learn. Human experts, well, they determine a hierarchy of features to understand the differences between data inputs. So, if I showed you a series of images of different types of fast food like pizza, burger, and taco, you could label these in a dataset for processing by the neural network. A human expert here has determined the characteristics which distinguish each picture as the specific fast food type. So, for example, it might be the bread of each food type that might be a distinguishing feature across each picture.

Now, this is known as supervised learning because the process incorporates human intervention or human supervision. Deep machine learning doesn't necessarily require a labeled dataset. It can ingest unstructured data in its raw form like text and images, and it can automatically determine the set of features that distinguish pizza, burger, and taco from one another. By observing patterns in the data, a deep learning model can cluster inputs appropriately. These algorithms discover hidden patterns of data groupings without the need for human intervention and are then known as unsupervised learning. Most deep neural networks are feed-forward. That means that they go in one direction, from the input to the output. However, you can also train your model through something called back-propagation. That is, it moves in the opposite direction, from output to input. Back-propagation allows us to calculate and attribute the error associated with each neuron and allows us to adjust and fit the algorithm appropriately. So, when we talk about machine learning and deep learning, we're essentially talking about the same field of study.

Neural networks, they're the foundation of both types of learning, and both are considered subfields of AI. The main distinction between the two is the number of layers in a neural network, more than three, and whether or not human intervention is required to label data. Pizza, burgers, tacos. Yeah, that's enough for today. It's time for lunch. Oh, oh, before I go, if you did enjoy this video, here are some others you might also like. If you have any questions, please drop us a line below, and if you want to see more videos like this in the future, please like and subscribe. Thanks for watching.

Machine Learning vs Deep Learning

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IBM Technology

딥러닝은 인공 지능의 하위 분야인 머신러닝의 하위 집합입니다.

머신러닝 알고리즘은 구조화된 레이블이 지정된 데이터를 사용하여 예측을 하는 반면, 딥러닝 알고리즘은 신경망을 기반으로 합니다.

발표자는 피자 주문 모델을 사용하여 의사 결정에 있어 입력 요소와 가중치의 중요성을 설명합니다.

머신러닝은 가중 입력을 사용하여 피자를 주문할지 여부와 같은 결정을 내립니다.

출력은 양수로, 피자를 먹는 밤임을 나타냅니다.

딥러닝은 신경망에 3개 이상의 레이어를 사용하여 학습 과정에서 인간의 개입을 줄임으로써 차별화됩니다.

딥 머신 러닝의 지도 학습과 비지도 학습 비교

지도 학습에는 사람의 개입과 레이블이 지정된 데이터 세트가 필요하지만, 딥 머신 러닝은 비구조화 데이터와 함께 작동하고 자동으로 특징을 결정할 수 있습니다.

딥러닝의 하위 집합인 비지도 학습은 사람의 개입 없이 숨겨진 패턴과 데이터 그룹을 발견합니다.

심층 신경망은 주로 피드 포워드 훈련을 사용하지만, 역전파를 통해 오류 계산 및 알고리즘 조정이 가능합니다.

신경망은 AI에서 두 가지 유형의 학습의 기초가 되며, 주요 차이점은 레이어 수와 데이터 라벨링에 대한 인간의 개입 필요성입니다.

이 동영상은 음식에 대해 간략하게 언급한 후 시청자가 '좋아요', 구독, 질문을 하도록 유도하는 참여 요청으로 전환합니다.