Overview and standards Selecting transcript lines in this section will navigate to timestamp in the video - [Instructor] The network fabric is one of the four main components of the IoT product. Its main job is to transport internal data from the hardware-defined product and external data from the external systems to the software-defined product. It consists of the OT network, that is the operat..
IoT value modeling Selecting transcript lines in this section will navigate to timestamp in the video - [Instructor] Smart products and connected products are predominant in IoT. Unfortunately, these types of products often fail because the incremental value is less than the incremental cost. Value modeling addresses this. It is a three-step process for creating valuable IoT products. Again, let..
Compare the three methods Selecting transcript lines in this section will navigate to timestamp in the video - [Instructor] In this video, we're going to take a step back and compare the three ensemble learning techniques that we've learned about in prior chapters. We'll review what we've learned about each, directly compare them all, and then we'll set the appropriate context to compare our bes..
What is stacking? Selecting transcript lines in this section will navigate to timestamp in the video - [Instructor] Now let's talk about the last of the three ensemble techniques that we'll cover in this course. And that's stacking. Stacking is an ensemble method that creates one strong metamodel that's trained on the predictions of several independent base models. On the surface, that may sound..
What is bagging? Selecting transcript lines in this section will navigate to timestamp in the video - [Instructor] Now that we've learned a little bit about boosting in the last chapter, let's dig into bagging. Bagging is an ensemble method that creates one strong model from a number of independent weak models, often trees, trained in parallel. This sounds pretty similar to boosting which isn't ..
What is boosting? Selecting transcript lines in this section will navigate to timestamp in the video - [Narrator] Let's dig into our first of three ensemble techniques that we'll be covering in this course. Boosting is an ensemble method that sequentially trains a number of weak models, often trees, to create one strong model. This sounds a lot like our general definition for ensemble learning, ..
![](http://i1.daumcdn.net/thumb/C148x148/?fname=https://blog.kakaocdn.net/dn/mVJMq/btrIy0jIDeq/lsKy2h1oVXvWBNPFyoXJtk/img.png)
머신 러닝이란 무엇입니까? 머신러닝이란 무엇일까요? 원점에서 시작합시다. 이 정의는 Arthur Samuel에서 나왔습니다. Samuels는 최초의 실제 기계 학습 개척자 중 한 명을 인정합니다. 그리고 그는 실제로 1959년에 기계 학습이라는 용어를 처음 사용했습니다. 그는 기계 학습을 명시적으로 프로그래밍하지 않고도 컴퓨터에 학습 능력을 부여하는 연구 분야로 정의했습니다. 이것은 적절한 정의이지만 다소 모호합니다. 그리고 나는 그것이 몇 가지 정말로 중요한 개념을 설명한다고 생각합니다. 이렇게 정의하고 싶습니다. 머신 러닝은 함수를 예제에 맞추고 해당 함수를 사용하여 새로운 예제를 일반화하고 예측하는 것입니다. 이것은 알고리즘 또는 기계 학습 모델이 사용자가 제공하는 데이터를 기반으로 한다는 사실에 ..
- Total
- Today
- Yesterday
- Paper
- 알고리즘
- tag hello
- geometry
- quiz
- Shell
- pytorch
- LeetCode
- 통계
- 자바
- ML
- amazon
- 기본
- LECTURE
- linkedin-skill-assessments-quizzes
- Neo4j
- nvidia #gan
- 파이썬
- MachineLearning
- mongodb
- extract archive multiple files
일 | 월 | 화 | 수 | 목 | 금 | 토 |
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
7 | 8 | 9 | 10 | 11 | 12 | 13 |
14 | 15 | 16 | 17 | 18 | 19 | 20 |
21 | 22 | 23 | 24 | 25 | 26 | 27 |
28 | 29 | 30 | 31 |