Deep Learning for Image Segmentation with Python & Pytorch (Udemy.com)

This is by far the richest course I have ever taken on Udemy amongst courses I already finished . It is well structured and provided with neat introductions for each of the taught topics. Moreover, the labs and exercises help a lot in getting your hands dirty with coding. Thank you Mazhar.

I learned a lot of high level concepts that I will no doubt use in the future, but this course needs work, such as improvement in editing. Furthermore, idk if it's because the course is outdated and the deep learning libraries have improved but from what I've seen working in industry these aren't codes that are used in practice, which is what I was looking for when I signed up for this course.

Very long explanations of dull power point images on what can be the outcomes of image segmentation. Examples or real world application would be much more interesting to illustrate why this is so important instead of just saying in what context it can be used globally.

I enjoyed the part of showing how different encoders and decoders can be combined to create various segmentation architectures. The course gave a good overview of segmentation models and the provided code is helpful for integrating into your own code. It would have been helpful if the examples elaborated on datasets with different structures based on the task and examples of transfer learning / fine tuning the encoders.

Exceptional course on image segmentation with python. Instructor clear explanations and hands-on examples make complex concepts easily understandable. Highly recommended for anyone keen on mastering image segmentation techniques using deep learning.

The instructor demonstrations were clear and on point, and the course covered a wide range of information related to image segmentation with PyTorch. Excellent course, highly recommended for anyone interested in this topic.

Very interesting course, with clever use of Python software. My only complaint is that parameters were often chosen for various functions without fully explaining the effect of the parameters and why the particular values were chosen. Fully explaining the effect of all the parameter choices would probably make the class too long, so a good compromise would be to include a reference to find out more about the various functions and the available parameter choices.

Overall course structure is good. Explanations can be better. Presenter can practice taking more legible pauses between sentences that will make delivery more effective.

This course start with an introduction to the basics of segmentation using deep learning and then move on to implementing, training and deploying segmentation models with Python. I really recommend this course to learn segmentation from basics to advanced.

Course material is well structured and easy to follow. The instructor is knowledgeable and provided clear demonstrations of the concepts. Hands-on coding exercises with python and pytorch helped solidify my understanding and gave me confidence to apply what I learned to real world projects. Instructor also provided resources for further learning. Overall, I highly recommend this course to anyone interested in diving into the world of semantic image segmentation with deep learning.