Simply amazing! This course felt so engaging and easy. And it had concepts that were taught so well that it felt easy. The concepts learnt in this course are a foundation for building a career in Machine Learning. I learnt about using Conv Nets, Image Augmentation, Dropouts, Transfer Learning, Multiclass classification. Thanks to Laurence Moroney for this wonderfully built course!

Generally a good introduction to convolutional neural networks for computer vision, along with the use of generators for data inputting. Also the use of image augmentation was well covered, and the section on using pretrained models where you freeze training on most layers is very important since those pretrained models are often the best to use for practical applications.

Very brilliant course. Lectures are short and crisp, coding assignments are excellent to get you started with dealing real world use cases. Since this course deals with implementation in Tensorflow, i would say, do the Deep learning specialization offered by Deeplearning.ai first and then do this course to glue both the theory and practical implementation together.

I want to say a huge thank you to the developers of this course. Since the systematization of the material was very impressive, the transition from simple to complex and the ability to explain the complex and visualize it using accessible and simple examples. I really liked the final task, which requires attention and understanding of the material. Cool course!

This course gives me an overview in CNN applying into various fascinating Computer Vision problems, which really excite me. The inspiration that I got would definitely push me to working harder in order to have a successful career as a ML engineer. Also, the teaching style of Laurence is one of the highlight for the course as I found it both fun and effective.

Initially I found the few weeks of the course very basic. I was wrong, I think the structure of the course is to introduce new Deep Learning enthusiasts to the field and it builds up very well. By the time you get to week 4, you are so comfortable with the concepts that it becomes second nature and then there is a twist. I loved it, hence I give it a 5 star.

As a follow up of the course "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning" and as an introduction of the convolutional neural networks for the case of image classification, again, the course is great. The content and the exercises in this course are more challenging and more entertaining to design/program.

This course is a great addition to the deep learning courses by Prof. Andrew Ng. I got to learn the fundamentals of deep learning from Andrew Ng's courses and learned to programme from here. It's a great course to learn Tensorflow and this course also helped me in my final year project. I'm really thankful to Coursera and deeplearning.ai for this course

I have certainly enjoyed taking this course. The instructor has been so good at keeping us interested in the course. It didn't really felt like learning. I have learned so many awesome things in this course to help me with the current job as well as inspired me to do some fun work on the photos I have taken myself. Thank you for this course. :-)

The course is marvelous explain and with clear, concise & straight forward concepts alike the practice project.

Take your time to understand the concepts, so you can move on.

I'll recommend to watch the specialization of Neural Network from Andrew Ng, to deeply understand the "magic" ( linear regression, matrices, derivatives) of Neural Networks.

I originally expected to learn more pure TF related stuff. But instead I learned Keras. Data augmentation with Keras is quite easy. Transfer learning is also easy to do if there is Keras model there already. But I do hope to learn a pure TF tutorial that are more common when you download other people's TF model and practice with your own data.

Very good course and a good sequel to the first course. These courses give what we need to try our own projects. The course doesn't teach much theory, but it makes us interested and make us search and try to learn on our owns. The notebooks provided in this course, however, aren't as good as the notebooks provided in the first course.

Convolutional Neural Networks in TensorFlow" by deeplearning.ai on Coursera is an excellent course that provides a comprehensive introduction to CNNs using TensorFlow. The practical assignments on Google Colab are well-designed and enhance the learning experience. Highly recommended for both beginners and experienced learners

Muy instructivo y activo. A uno como estudiante lo obliga a interiorizarse de verdad en los conceptos para comprender mejor las etapas que se deben implementar para el tratamiento e implementacion de una red neuronal convolucional. En general, con explicaciones claras y comprensibles puedo decir que este este un curso muy bueno.

This was the actual dealing with the dataset saved at local memory location rather than predefine dataset where the dealing with label and directory were ignored which learner actually face problems while learning and handling with the datasets stored at local drive. well this course actually helped for my major year project .

Precise and to the mark. Good brief up of the concepts. 5 stars for ease of implementation through programming assignments. Suggestion to fellow learners: Couple these courses with those by Andrew Ng, so it would be the best merger of theory + implementation. Laurence Moroney never fails individual's expectations. :)

This course presents a more in-depth look at CNNs in comparison to the first one of this specialization. Subjects as Image Augmentation, Data Generators and others are taught about. The only thing I didn't find quite right is the final assignment. I could be wrong here, but it seems it wasn't designed properly.

Easy to follow. a lot of examples. I was expecting at least one assignment for the final! :)

As for the convolution we never talked about DOG+SIFT or other feature extraction techniques. Also I would like to see how we can separate an object of interest from background e.g. using clustering or a video stream.

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.