学生对 DeepLearning.AI 提供的 Custom Models, Layers, and Loss Functions with TensorFlow 的评价和反馈
课程概述
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RA
Jan 6, 2021
I started this course with the intention of learning the syntax needed to implement VAEs. This course satisfied that requirement perfectly! Thank you :)
EL
Dec 13, 2020
This course was fantastic! After learning about the functional API, I found tensorflow/keras are far more flexible than I had realized and am much more excited about the possibilities.
226 - Custom Models, Layers, and Loss Functions with TensorFlow 的 228 个评论(共 228 个)
创建者 周昊轩
•Apr 20, 2024
For the last graded assignment, just keep the first and last layer. Otherwise, there will be a time off error
创建者 Federico C
•Nov 3, 2021
An interesting perspective, but the assignements modalities are a bit too easy.
创建者 Goran I
•Jan 4, 2025
I have a habit of reading course reviews before signing up for them. It helps me form an idea of its contents and decide whether to sign up and devote my time or not. I am seldom surprised much, but in the case of this course, I must admit I was amazed by the gap between the course rating and its content. I was aware, of course, of the 2-star review (by Irina G, posted on Nov 22, 2020) which, at that time, had by far the most votes: 18. The next most upvoted review (by Giora S, Jan 7, 2021) had 7 votes and it also gave only 3-stars. At that time, I was not alarmed by the fact that the most upvoted review giving 5 stars to the course had only 3 votes. Although it is somewhat surprising, I can understand that many people may feel good about a course just because it is easy to pass and, consequently, have a course like this have a rating of 4.9. But how does Coursera select top reviews with 5-stars and only very few upvotes is beyond me. This really makes me reconsider Coursera's rating system. I fully agree with Irina G's review. Could add few more observations of mine, but I do not see the point. I really appreciate the mission of Coursera, the effort made by the lecturers to prepare a course and the fact that we can listen to it for a comparatively very low price. But having Laurence read pre-written text from a screen with generic statements, explaining virtually nothing other than reading the code taken in large part from Keras tutorials which, at least, have explanations, is quite surprising. Even more so considering its rating is pumped up to 4.9.