I love this, because the instructor make the difficult easy. After ending this course, I believe I would enrolled on the other specialization, to gain a better mathematical understanding of convolutional neural networks but I'm pretty happy to learn the practical stuff, this make possible a lot of projects!

This course builds on the previous introductory course in the Specialisation. Not only do the four exercises provide practice towards neural network implementation, they also provide a chance to use Python for organisation and manipulation of data, pre-learning.

A fantastic and concise course over all.

I mainly to learn the various constructs to do various things in TensorFlow, and this course is very well constructed for it. It doesn't explain the actual mathematics though, and I don't blame it for that. It is just designed to help people learn the framework. Overall, a very satisfying experience.

This course is a very good introduction to Tensorflow and CNN. I have taken Machine Learning theories at school and this is a very nice **programatic** supplement to my course. I think this would be even more helpful if I took it before I learn the theories. I would have been in less trouble then.

It's amazing how far we've come in image processing. I remember using basic filters like sobel edge detector during my undergrad. And now we are here, being able to get SOTA results in just few minutes. I wonder how those Phds who were working on handcrafting filters ~2010 would have felt.

This course in tensorflow specialization is a must recommended. It builds knowledge from beginners to advance very smoothly, You will be able to get a experience of how to begin coding for tensorflow also be able to understand its core layers, And learning from Laurence is always fun.

Very well thought through course for Convolution Neural Networks using Tensorflow, covering some of advances topics like transfer learning, callback and review convolution layers. I already had understanding about CNN and these topics. This course shared scenarios when it is used.

The course was amazing, but the thing i don't get it is the 'sparse_categorical_crossentropy' must be use in the last exercise notebook. In the video(s) ,Laurence introduced only 'categorical_crossentropy', hope somebody could upgrade the notebook to suitable the lessons. Tks.

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Really good course, but recommend doing deeplerning specialization first before doing this one or doing them together. Because Andrew Ng explains really well how convolutions work, and without this background info, it will be difficult to understand the concepts in this course.

Really impressed by the work of the team. It is designed specially for the beginner to advance their career and be expert the emerging AI field. With the help of high quality videos and project based assignment one become expert hoe to deal and tackle real world problems.

Excellent. I learned after a couple of years working with neural networks new topics and implementations. I think it would be a good idea to include also here an exercise that gets graded at the end such that we take our time and can try out if we can make things work.

The week 1 is a bit casual but where as the remaining one's are just awesome learnt a lot like how to implement a model without overfiting and learnt how to implement transfer learning and multi-class classification problem, really worthy taking up this course....!!!

This course is for beginners and intermediate, If you know the detail of the model layer then don't take this course. The instructor only tells the code who to add Conv, pool max layers in TensorFlow do not explain the depth of convolution and pooling layers.

This was a really great course for me to dive into practising the implementation of machine learning for image datasets. The instructor is really nice. I thoroughly enjoyed the course and will be taking more courses on applied machine learning from Laurence.

The course was nicely built on the advanced topics of multi-class classification, data-augmentation, and transfer learning in Convolutional Neural Networks. Special congratulations to the instructor and his team for coming up with such a nice course.

The course was excellent. Other than the (typical by now) Coursera code-submission issues, the course really covers a broader range of CV applications & TF capabilities than I've seen with the "get it working and move on" workflow at the day job.

Thank you very much for this course, it helped me understand data augumentation and transfer learning. I am very inspired seeing computer graphic generated training data. It would be great having a separate course/workshop covering this topic.

This is very well structured course for geeks who want to start learning machine leaning and implement different neural networks are hiking the technology world.

I personally appreciate the course material and instructor for the immense work.

Thank you for teaching me this outstanding course I learned a lot about Convolutional Neural Networks. The programming assignment were also at the right difficulty not too hard and not too easy. The quizzes were easy but really awesome.

Thanks

An excellent learning platform during Covd-19 pandemic. I appreciate the effort of the Coursera team who provide us such an amazing learning environment. This course really helps me to improve my practical knowledge of CNN.

Thanks Coursera.

This lectures are givin a very nice advices for practical implementation of ConvNets. combining it with prof.Andrew Ng's lecture exercises in this course will allow you much more practi implementation of knowledge you have acquired before.

The course in general is pretty good, only the last test seems to me that is incorrect, since there are 24 different classes, but doesn't approve it when you set it with these 24. It requires from you to put 26 when to me seems illogical.

I found the course really interesting and I learned a lot. The thing I liked the most about this course is the minimal helping nature of the evaluative notebooks, deep learning specialisation's notebooks practically spoon-fed the answers.

The practical aspect of this course is addicting. I can't stop myself from wanted to try the next technique. maybe because i have seen most of these before but i going had made it clear what i was doing wrong in some of my "failed kaggle"

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand