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Deep Learning and Reinforcement Learning(으)로 돌아가기

IBM의 Deep Learning and Reinforcement Learning 학습자 리뷰 및 피드백

67개의 평가
14개의 리뷰

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

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.

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 14개 리뷰 중 1~14

교육 기관: Gideon D

2021년 4월 24일

good course, PROS: very well presented, clear amd methodic. appropriate tasks. CON the name suggests that RL would be a significant topic, while in reality it appeared only in the end of the course and important subjects such as TDlearning are missing.

교육 기관: Seif M M

2021년 1월 12일

Reinforcement Learning part needs to be a separate course and more details in it

교육 기관: Ashish P

2021년 3월 29일

Well prepared, gives a good intro to multiple Deep Learning algorithms and good examples to cover the major topics. A few more practice labs on CNN and RNN would have been awesome!

Cons : The only difficulty I found was with the english accent of our dear trainer. Sometimes it was really very difficult to comprehend what was being said and one needed to rewind the video multiple times and read the subtitles. Other than that, nothing to complain.


교육 기관: Yasar A

2021년 4월 21일

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

교육 기관: george s

2021년 9월 7일

Extraordinary course, one of the best in coursera!, Reinforcement Learning and Autoencoders can have better examples.

교육 기관: Luis P S

2021년 6월 21일

E​xcellent from the theory and the practice! Great explainatory videos and detailed jupyter notebooks!

교육 기관: Jose M

2021년 2월 9일

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

교육 기관: My B

2021년 4월 30일

The difficult terms are simplified enough for understanding and application in real life.

교육 기관: Pavuluri V C

2021년 9월 24일

this course is awesome

교육 기관: Volodymyr

2021년 8월 22일

Well balanced course

교육 기관: Neha M

2021년 3월 29일

Excellent course

교육 기관: Bernard F

2021년 3월 18일

Very good. I learned a lot but the subject matter is quite extensive.

교육 기관: R W

2021년 7월 26일

This course has a larger scope than the other ML certificate courses and is a little out of date. While it introduces RL, it does not discuss TD learning or Deep RL. RL seems "tacked on". Similarly, there is a brief introduction to Attention, but no substantial discussion of Transformer models (I suggest dropping LSTM and talking just about Transformers). Unlike the other courses, which introduced the concepts and also covered practical steps on using these methods, the DL/RL course is a little light on the practical side of DL. There is little discussion of why particular architectures are chosen for specific problems or how sensitive those architectures are to various hyperparameters. You will know what DL, CNN, RNN (and to a lesser extent, RL) are is when you finish this course, but there's a big gap for any practical use of these tools, which was less of an issue for the (admittedly simpler/more scoped) topics in earlier courses.

교육 기관: Rui T

2021년 11월 3일

the content is presented in a bullet manner without any deep dive into any algorithm. You can get a good overview of various models in DL and RL, but nothing in details. I would not recommend any DS to use this course as a learning module. But maybe it is quite suitable for people without data science background. Even though, the presentation is not interesting. Just read-out of bullet points for each model.