easy bb

helpful

Great!

NICE!!

作业过于简单

Useful

great

Good!

Nice

good

bugs

good

good

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ok

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I've loved Andrew Ng's other courses, but this course was boring and not well-organized. The lectures were unfocused and they rambled a lot; they're nearly the opposite style of Prof. Ng's other material, which I found extremely well-organized. Most topics could be shortened 33-50% with no of clarity.

The course structure itself could use improvement:

The first part of Week 3 (Hyperparameter Tuning) belongs in Week 2.

The third part of Week 3 (Multi-Class Classification) should be its own week and its own assignment and could really be its own course. This is *THE* problem that almost every "applied" machine learning paper I've read is attempting to solve, whether by deep learning or some other class of algorithms. (Context and full disclosure: I'm a Ph.D. Geophysicist and my research is in seismology and volcanology.)

The introduction to TensorFlow needs to explain how objects and data structures work in TF. It really needs to explain the structure and syntax of the feed dictionary.

In the programming assignment for Week 3, there are three issues: (a) The correct use of feed_dict in 1.3 is completely new and cannot be guessed from the instructions or the TF website, and it's not clear why we use float32 for Y instead of int64; (b) In 1.4, "tf.one_hot(labels, depth, axis)" should be "tf.one_hot(labels, depth, axis=axis_number)". (c) In 2.1, the expected output for Y should have shape (6,?), not (10,?).

As an old school (80s) software developer I feel uncomfortable about the lack of formal teaching on the structure and principles of TensorFlow. Sure, I can write the code and fly through the programming assignment, I "kind of" get it, but for a thorough engineer, that "kind of" creates a sense of unease. I wish Andrew Ng, being the incredible practical teacher he is with the theory of Machine Learning, would have spent a bit more time reviewing that particularly practical topic of TensorFlow more in depth, because 1h on it would bring much more value than say, understanding the inner working of batch norm, especially to an engineer ready to onboard a new project and start creating. For example, when should you use a placeholder vs a variable and why? Why is there a "name" parameter in the constructor of a variable, when should I make good use for the difference between the name at a tf level and its actual Python variable name? etc... Unlike Matlab or Numpy, TensorFlow looks to me like it could use a bit more theory before practice. Next class? :)

Nice explanation of Adam. Extremely minimal introduction to tensorflow; I felt unprepared to deal with all programming error messages I encountered when using TF. I would have liked to have had more exposure to softmax outputs as well; the multi-class case is new here. My biggest complaint is that there was quite a bit of time spent trying to explain batch normalization and no corresponding programming assignment. Also, in the past I felt I had my hand held a little too much in the programming exercises, whereas when tensorflow was introduced I felt I'd been thrown by that hand into the abyss; the expected output could not help me debug because it seemingly was designed to remind me over and over that tf.Session.run was needed to give value to tf variables. ya... I think you guys have some work to do on this course.

Very mixed feelings about this course. The course title and nearly all (but 20 minutes) of the video content are on the topic of hyperparameter tuning, regularization and optimization of neural nets. This material is excellent. However, the programming assignment for Week 3 is about building a simple model in Tensorflow, with no coverage the rest of the material from the week. It is as if they included the wrong assignment, or just forgot to include the appropriate assignments to practice the actual content of the course. In addition, the Tensorflow intro in the videos and the Tensorflow assignment are not that great an introduction to the concepts behind Tensorflow. There are much better tutorials available on the web, such as from Tensorflow.org and codelabs.developers.google.com

Please update course to use / teach tensorflow 2 syntax

Also this course... really holds your hands through the programming exercise. The code in each exercise is well organized into separate subfunctions, each of which has its own check, so its already simple enough to debug. But when this is couple with the fact that the instructions basically spell out what lines to put where (and give significant hints as to what functions to use in those lines...) the whole thing is completely trivialized. I think that I at least would have learned much better if I was forced to use man pages etc. to look up the usage of functions, for example.

This course was much less rigorous and theoretically-grounded than the first. There didn't seem to be much justification for any of the techniques presented, which was a stark contrast to the first course.

However, the topics are important and useful to know, so I'm glad they were covered. To me, the most useful sections were on softmax regression and deep learning frameworks, which I really enjoyed. The TensorFlow assignment was also interesting and (relative to the others) challenging.

I think there is a lot of scope for this course to be improved and I hope Dr Ng and team will do so in the near future.

The content is didatic, as well as the explanations, a reasonable course for a real beginner. However, the material is one of the worst I have seen: a lot of errors that are indicated with notes between the classes and MANY annotations and sketches from Andrew in the slides. It mixes up handwritten annotations with digital text, a complete mess. I had to edit the PDF in order to make something useful, even though a lot of information is either missing or floating somethere in the slide. Besides that, some videos are not edited properly: it is possible to experience many repetitions of the same phrase.

(+) On the plus side: Andrew is always an excellent lecturer. Also, the python notebooks provided for the assignments are an extremely good guidance for structuring a deep neural network project.

(-) On the minus side: this course is rather disappointing compared to Andrew's well-known Machine Learning course on coursera. There is basically no challenge, as assignments (or, I would call them, "tutorials") are *very* guided: they can be completed even with a very shallow understanding of the content. Also, lectures are quite repetitive, and more like a practical cookbook than an actual course.

Nice course, but again, main emphasis on the practical side and 'never mind, you don't need to know the details' approach. Having optional parts where theory about batch-normalization implementation and softmax derivative derivation could be shown - that would be very desirable. Another not so great thing is that final TensorFlow-related practice exercises are too 'quick' in a sense that 99% of the code is written for you and hints are given in such a way that you literally don't even have to use a half of your brain. That is also frustrating, when everything is already done for you.