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

Excellent course, in particular, all explanations to work with the Image Augmentation libraries, I enjoined the transfer learning part, highly recommended for anyone looking to improve their knowledge of Convolutional Neural Networks

T​his course is really really challengging until I have to repeat 16 times . But because i try, try and try i can finish this course.However is late, but i want to do for study and learning more to the Data Science and Data Analyst.

It is a great course to learn about Convolutional Neural Networks, exploring how to use them with large datasets, Augmentation, Dropouts, Regularization and Transfer learning, and coding during binary or multi-class classification.

Deeplearning.AI allows me to explore more about CNN. Using CNN and Tensorflow, I can build a model in just few lines of code. This library really helps me to overcome the problems in Machine Learning, especially in Computer Vision.

The specialization covers brief introduction to the concepts of Computer Vision and Natural Language Processing. It introduces to TensorFlow and gives a hands-on practical experience over the tool through simple assignments.

Learnt a lot of new things about image classification, how to better predict images using TensorFlow. Laurence Moroney is a great teacher who skillfully explains the code and its significance in an easy-to-understand manner.

Incredible course structure. Really well designed and thoughtful. The programming assignments were especially very helpful. Grateful to Coursera for letting me do this specialization during these uncertain times of COVID-19.

It's a nice approach and a good example of how we're going to do Machine Learning stuffs through an open-source library called Keras from Tensorflow (from Google ofc). Thanks, Google and DeepLearning.AI., Mr. Andrew Ng!

Very instructive and practical, but the coding assignment can be mis-leading from time to time. However, that is not anything out of ordinary, practitioners should expect some level of sophistications in real life

Coming from an aspiring Data Scientist, Laurence Moroney provided succinct explanation on practical aspect for CNN, which is a definitely a supplementing course material alongside Deeplearning.ai's specialisation.

very well explained and the google colab notebook that they share is very helpful . i recommend to go through the lectures and go through youtube videos for deeper understanding before just jumping to exercises.