A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!
great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.
By Alejo G
•A lot of boilerplate code with few new concepts
By Mikołaj M
•The course covers elementary techniques.
By Victor S
•Useful course. Just a bit unstructured.
By Bojiang J
•Content too easy and not engaging....
By Navid H
•I wish it had real assignments
By Samyak J
•exercises are not very clear
By Paula S
•course is a little too easy.
By Pallavi
•It was not great and good
By Yuxuan C
•A little bit too easy.
By Luiz C
•not challenging enough
By Victor M
•Contenido superficial
By Igors K
•I wish it used TF2.
By Masoud V
•Useful but too easy
By Ruxue P
•Too little content
By Gerard C I
•to much shallow
By Rob S
•Good course
By Neshy
•too basic
By Mohammed I A T
•just ok
By Thomas R
•Materials were good for someone who has taken university courses on convolutional networks, but labs were extremely poorly done. Final lab of the course was missing sections for the data generator flow method calls, and augmentation wasn't even tested for. Marker could be improved and provided code can have better sections and maybe an explaining markdown at the top rather than going back and forth. I also noticed that accuracy changed from logs.get('acc') to logs.get('accuracy') which seems to be a tensorflow version issue. I feel overall like the course has been abandoned.
By Li P Z
•If you have taken Andrew's courses in ML or deep learning, you will be disappointed. The amount of content in the videos and exercises is shrunk down by 75% per week. I think a much better job could have been done of structuring the course, and creating meaningful exercises. The instructor does an OK job of showing you how to use TF, but he doesn't always explain things very clearly, and doesn't always have an accurate understanding of how ML or deep learning works.
By 黃文喜
•Content is really useful, but the assignment is really really bad and not user friendly(actually it drives me crazy). For example, instruction is not clear, parameter is outdated(still use 'acc' for accuracy?), assignment cannot be graded not because of modeling. These inconvenience obscure of the importance of learning CNN in TF. For this reason I don't think this course worth more than 3 stars.
By Rishi R
•This course could have covered many more topics in detail, like visualizing individual layers, performing style transfer, saving and loading models, etc. All these were skipped and weeks were wasted on a simple extension of a small concept (image augmentation and multi-class learning) which anyone who glanced at the Keras API could have learnt. I am disappointed at this course frankly.
By Tran N M T
•Really a bad course. Most of the materials can be found online for free on TensorFlow official documentations. Many practices are outdated. Problems with the coding assignment are a nightmare. There is no supervisor to answer many common questions. The code grader checks for very particular things and instructions were not clear at all. In general, this is a pretty bad course.
By Ian P
•The first and fourth graded assignments were not very well posed. The grader in the 4th graded assignment kept running out of memory. The instructors do not get back to people in the forums. There was not much actual new material: most of the 4 weeks of material could have been covered in a single week. This has been the most discouraging coursera course i have taken.
By Ayush M
•Course Material not detailed enough and expected more from it. It does not contain enough variety in exercises and lacks a lot of concepts.
Anyone with good learning (and "overfitting") can complete 1 course in a day.
Final assignment lacked a lot of use case description and it did not even tell us anything about the data or recommended parameters for training.