Mar 15, 2020
Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..
Sep 12, 2019
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.
автор: Henrique C G•
Jan 02, 2020
I'm sad to say that I'm really disappointed with the course. What is even stranger is that professor Andrew is associated and endorse the course. I like professor Marooney, but honestly, even his free tutorials on the Tensorflow channel on Youtube have more information than this course. It really seems like something put together in a haste just to make it available on Coursera. The level of detail and instructions is not on par with the quality of both the Coursera platform and the professors associated with this course.
It seems that as I progress through the courses in this specialization the instructions get poorer and poorer and the level of information gets more and more scarce. It got to a point where we are just given notebooks to run; they are not even graded (they barely were on the first course). And even the notebooks where the we are given a chance to complete some code, there are absurd things like "print(#your code here#)" in places that don't even make sense except if we copy and paste from the other notebooks of the course. Really? Print what? The only way we can guess what kind of debug info the notebook is asking is by looking at other notebooks at that exact same line.
For the reviewers; if you are really reading this, please remember that Coursera is charging $49/month for this specialization. If we consider that an average student will take 4 weeks to complete, that's almost $200 for something that's barely a tutorial at it's current version. $49 may be a reasonable rate for a citizen of the US, for example, but it's and exorbitant amount of money for students of poorer countries using the platform in hopes of acquire knowledge of decent quality.
Jul 26, 2019
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.
автор: Muthukumarasamy S•
Aug 04, 2019
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.
автор: Artem D•
Jan 29, 2020
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.
автор: Shehryar M K K•
May 03, 2020
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.
автор: Zhuang L•
Apr 20, 2020
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.
автор: Thomas B•
Apr 10, 2020
This course teaches you how to apply CNN to image data, how to augment image data with ImageDataGenerator, and how to do transfer learning. It is very easy to follow, and quite possible to finish in half a days worth of effort. It would be nice to be more explicit with what is required by the grader, as assignment instructions not always are clear.
автор: Bakhtawar U R•
Dec 09, 2019
Good but too basic.
Specialization's first course already covered the basic of tensorlfow. This course is suppose to expose to sota topics in computer vision using cnns. The content in this course can be easily fetched from many online forums. Thus the curators need to put some advance topic like attention, spatial transformer etc etc
автор: Philip D•
Sep 05, 2019
A good course, but again, not nearly as in depth as the original deeplearning.ai set of classes. The material feels introductory and at times superficial, with no real work required of the student to complete the class. At best a very early start to using convolutional networks with the keras apis in tensorflow.
автор: Agam S•
May 31, 2020
I learnt a lot about CNNs and how to implement them, but I was taken aback to see advanced coding concepts being used in the programming assignments. I thought the concepts taught in the course itself were to be used only, but some parts of the assignments had parts which were too much to grasp well.
автор: Pete C•
Feb 20, 2020
The course was very repetitive, not challenging, and therefore not particularly helpful. Andrew Ng's Deep Learning Specialization is vastly superior. Aside from getting used to TF and CoLab, I'm not sure what this helps with. I found it odd that it was recommended to me after the DL specialization.
автор: Giulia T•
Apr 27, 2020
This course is a really light introduction with CNNs in TensorFlow. While I enjoyed the videos, the content feels far too shallow. I completed the course in a couple days (and I'm not an expert in the field). It felt more like having gone through a TF tutorial than a grad-level MOOC
автор: Raul D M•
Nov 01, 2019
It is a good course for a fast overview on this topic. Be aware that it is not an introduction on ConvNN (but there are several courses of deeplearning.ai on this topic). If you are looking for a detailed course on Tf for ConvNN, I suggest you a book, the official documentation.
автор: Ambroise L•
Dec 29, 2019
What could improve it: Not enough depth in the practicals if you have already done Andrew Ng's course on Conv nets. No graded practical exercise.
What was good: Clear examples, Good setup to experiment with the algorithms & Speak explains concepts very clearly,
автор: Ignacio R L•
Mar 28, 2020
Good course, but the notebooks need a deep review to fix the problems related to balance between the requirements of the exercise and the resources available also a better explanation of the exercise aims would be a nice to have to avoid misunderstandings
автор: Michael R•
Sep 18, 2019
Actually a great course. Only not getting more stars due to the issue encountered with the last exercise where there is an issue in loading the data files. The workbook keeps on crashing and there is no solution provided to resolve that.
автор: Matías F B•
May 28, 2020
The material is good, but there is not much thereof.
The duration of the assignmentsis greatly exaggerated, since most of the lengths for the readings and exercises are wrong.
The course can easily be done in 25% of the official time.
автор: Dirk H•
Nov 07, 2019
If you have taken the first course of the specialization this class was repetitive at some points. I also did not like that there have not been graded coding problems. I still got some practice and learned some new techniques.
автор: Wenyu Y•
Mar 20, 2020
The materials about implmentation of transfer learning is helpfu, but again, I think the whole content of the first two courses could be compressed into one week. There're really not too much new things.
автор: Sumit c•
May 18, 2020
some clear instructions should be given for students. In exercise of week 4, there was no specific instruction about using .flow instead of .flow_from_directory, for labels we had to use to_catagorical.
автор: Amir S•
May 25, 2020
Course assignments need a good overhaul. The two environments to practice the assignments (Jupyter workbooks and Google Colab) are not consistent, one throws an error while the other one works fine.
автор: Nermeen M•
Dec 13, 2019
Very good course but please consider reordering the videos and reading especially in week 3. It is better to discuss the code in the video before moving to the notebook not the opposite.
автор: Ashok N•
Jun 26, 2020
Course content was super nice.
But exercise organization is very annoying. not at all satisfied with the exercises. sometimes not loading and sometimes is really annoying . very disappointed
автор: Renjith B•
Jul 15, 2019
Good content for classification tasks. But didn't cover anything related to object recognition, localisation and semantic segmentation which are the challenging computer vision tasks.
автор: Yuvraj G•
Apr 11, 2020
Too basic course. If its a practical course, then there should be exposure to more functionality of keras and not just the basic one which can be done from a blog/documentation.