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Deep Learning and Reinforcement Learning(으)로 돌아가기

IBM 기술 네트워크의 Deep Learning and Reinforcement Learning 학습자 리뷰 및 피드백

4.6
별점
108개의 평가

강좌 소개

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics....

최상위 리뷰

YE

2021년 4월 20일

The concepts were clearly explained in lectures. The assignments were very helpful to gain a practical insight of the skills learned in the course.

JM

2021년 2월 8일

Hello, thank you again for the course. My congrats, once more, to the instructor on the videos!

필터링 기준:

Deep Learning and Reinforcement Learning의 19개 리뷰 중 1~19

교육 기관: Gideon D

2021년 4월 24일

교육 기관: Rui T

2021년 11월 3일

교육 기관: Seif M M

2021년 1월 12일

교육 기관: Ashish P

2021년 3월 29일

교육 기관: R W

2021년 7월 26일

교육 기관: Bishal B

2022년 4월 4일

교육 기관: Yasar A

2021년 4월 21일

교육 기관: george s

2021년 9월 7일

교육 기관: Luis P S

2021년 6월 21일

교육 기관: Jose M

2021년 2월 9일

교육 기관: My B

2021년 4월 30일

교육 기관: Marwan K

2022년 3월 30일

교육 기관: Pavuluri V C

2021년 9월 24일

교육 기관: Volodymyr

2021년 8월 22일

교육 기관: Surbhi J

2021년 12월 18일

교육 기관: Neha M

2021년 3월 29일

교육 기관: Subhadip C

2022년 1월 31일

교육 기관: Bernard F

2021년 3월 18일

교육 기관: José A G P

2022년 5월 18일