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學生對 IBM 提供的 Deep Learning and Reinforcement Learning 的評價和反饋

68 個評分
14 條評論


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....



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


Excellent from the theory and the practice! Great explainatory videos and detailed jupyter notebooks!


1 - Deep Learning and Reinforcement Learning 的 14 個評論(共 14 個)

創建者 Gideon D


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.

創建者 Seif M M


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

創建者 Ashish P


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.


創建者 Yasar A


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

創建者 george s


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

創建者 Luis P S


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

創建者 Jose M


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

創建者 My B


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

創建者 Pavuluri V C


this course is awesome

創建者 Volodymyr


Well balanced course

創建者 Neha M


Excellent course

創建者 Bernard F


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

創建者 R W


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.

創建者 Rui T


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.