Super cool stuff!

Good for beginer

Very informative

Great content!!

very very good

Voice is low

Excellent!

quite easy

Too easy.

Very Cool

Was okay

Good!

Good

Good

I had a problem with the weekly assignments 1 and 4, which I think asked for things that were not presented at all in the videos. On the first week, without any preparation, we were asked to create the directories, supposedly without any python background, which was tough. But that was just a couple of commands. In the last weekly assignment, the file reading and loading was very problematic for those who hadn't previous python knowledge, and pretty advanced too. In the course of the lessons and the notebooks shown previously, never had we encountered .csv files, and the way to load the images that we were introduced to, with directories, was not at all present in the final project. Also, the methods that were needed afterwards (.flow(), evaluate) were not even hinted before, even in the comments of the assignment, if not before, during the videos/notebooks. All in all, the last exercise took me by surprise and was really tough to get working, because the course was almost irrelevant to it (no transfer learning or directories, the two main points in the course).

A bit too basic and shallow in terms of conducting the lecture. You are left doing most of the things on your own as the trainer assumes you know. Like using the jupyter notebook, configuring the tensorfow. Some of the google collab books do not work or took too long to load, the videos are too short no notes provided at all. After finishing the course there is nothing to refer to and its starting all over again. Given the level of machine learning course with Professor Adrew Ng, the standard is very high and you will expect that same level. Nevertheless, the concepts are very useful and the lecture explain very well. There level of material left for students to practice on their own,like assignments, notes. Not to be referred to existing material.

Overall learning from this course is less compared to the expectations from a 4 week course. I was expecting to learn variety of TensorFlow implementations for CNN like Face recognition, Object detection. But this course only talks about Image classification. It would have been better if you could also discuss more about implementing various architectures in TensorFlow like ResNets, Inception. Also, You talked only about using sequential layers in Keras and concatenation of layers in Keras is not discussed here. I know all these concepts are discussed in Deep Learning specialization. I was only expecting to learn their implementation in TensorFlow from this course.

It's a nice next step after the first course in this series, however, I think a lot of this could be summarized in a shorter course or even added to course 1. I was particularly annoyed by some of the assignments as they required knowledge of other libraries that are not part of the course. Particularly Week 2 and 4, I spent a lot of time figuring out how different libraries worked just so I could preprocess my data before even gettin on to the course material. Week 4 in particular feels cramped up and the assignment uses a lot of tools not previously discussed, I don't think I learned much from it, I just wanted to be done.

I wanted to rate this course 5 stars as it really is the best intro to CNN's ive seen but the last assignment was so egregious that I just have to dock 2 stars to bring this to instructor attention. Lots of people are having this problem. It was not the difficulty (in fact it was a nice change of pace from the usual flow_from_directory assignments) but instead the marking criteria and timeouts etc. My first submission took 2.5 hours to fail. I submitted again with next to no changes and it passed within 5 minutes - I lost the time it couldve taken to do .5-1 week of the next module

I think it is too basic and not a lot of depth into what you are learning, specifically on the differences between binary and categorical classes, reading from disk vs manipulating data already in an array.

I was expecting more depth on tradeoff within Tensorflow's API choices. More direct comparisons between optimizers and the data generators. I feel like one straightforward exercise at the end of a really shallow week of videos is not enough to understand what students are really doing/learning.

The course lectures are solid, but the assignments are pretty dismal for beginners. There isn't much guidance built into the assignments, and sometimes they require the use of things that were absolutely not covered in the lectures(classic academic mistake). My suggestion for the course creators is to examine how Andrew Ng's assignments are in his Coursera course and model them after that. Or simply make sure that the assignments are clear(clear to someone beginning, not a TF expert).

I liked the lectures (videos). And I did not like that the course has no mandatory programming assignments. I pay for the course to make myself study. And I believe that there is no study without practice. Hence, this course did not make me study, thus I don't understand why I need this course :-(. And I could find free lectures about TF/Keras (maybe not so good, but free) and/or read the documentation. BTW, I really like Andrew NG's courses, but this one really disappointed me.

This course focuses on the teaching of TensorFlow modules related to CNNs and does a good job in introducing some modules of tf and keras for data loading and manipulation. However, it is very light on theory and is only helpful if Deep learning specialization is taken beforehand or in conjunction. Furthermore, this course will need some refresh soon for its modules as it is still using version v1.x of tf as well as some code re-organization.

The use of the .flow() method on the last exercise would deserve some explanations : the labels need to be transformed from sparse (int format) to one_hot (with tf.keras.utils.to_categorical for example), so the loss='categorical_crossentropy' actually works in model.compile().

There is no mention of the different ways of structuring the labels in the course, this can be misleading.

Other than that, good material.

The videos were quite solid. The programming assignments were poorly designed to accept identical answers, but not other solutions that work. This did not evaluate students' creativity and depth of understanding. The Jupyter notebook environment was quite fragile. The resources allocated for each notebook was quite limited. I expect more computer or human resources allocated for each student paying the tuition.