The Keras part of the course is more attractive just because its final assignment is much better structured than that of PyTorch.

I like the flexibility to pick our framework for the project i wish the kers one were a little bit more challenging

It is a great task but the tools delivered very complicated. It is sometimes better to use upfront your own tools.

I have completed this course but did not get the badge for it. Is there any way to access it?

Thank a lot for creating this course. It really useful and practical for me.

Buen curso, implementando todo lo que se vio en la especializacion

Nice idea, however it could be a little bit more elaborate.

This course was riddled with operational flaws regarding the image data, and how it operated in the IBM framework. At one point I was not able to run the labs with either PyTorch or Keras versions, and eventually just downloaded the notebooks and ran them in Google Colab to complete the specialization.

The data from the course is not well prepared, some invalid pictures in the data. And also sometimes the IBM platform can not run the training well, loss connection and need several hours of time for training the model...

This is a good enough project if it is your first Pytorch implementation. However, the program is unevenly difficult, with very few information for week3 assignment, and almost copy/paste assignment for week4.

there are some issues incl.

- IBM lite version crash (So I used my local GPU environment) - Want a more challenging project with friendly provided reference and help

The docker environment by IBM is horrible. I just got to finish my course running all the notebooks locally (except for those at the Watson environment)

Not well-prepared materials in Keras, especially in Week 3 (model-training) which took more than 3 hours to training and even not successfully.

some of the modele and code is outdated.

good course

The content of the course is very interesting and highly informative, however there is a critical flaw in this course (at least for the keras library side of things), the problem is that IBM Cognitive Labs, the intended environment for the assignments, is incapable of running the later labs (week 3 + final) and will crash after 30+ minutes of waiting, this being due to the instructors having us use a relatively large database of images (~250 mb). Jupyter Notebooks on IBM Cognitive Lab struggles to just unzip the dataset (which is downloaded as a zip), not to even mention fitting the models to the data, which I found to be impossible to do with IBM Cognitive labs (for both week 3 and the final assignment). Ultimately I ended up having set up a jupyter lab environment on my own laptop, the problem is even then it took about 14 hours to fit the data to the models (in total, both week 3 and final assignment).

TL;DR the instructors have us using a pointlessly large dataset images which serves more to test our patience than our ability to create deep learning models.

Very disappointing. The instructions are unclear in the assignments and it got frustrating choosing which platform to use to speed up the process and to bypass notebook errors. This was the least challenging and least interesting Capstone project I have done with IBM.

T​he information in this course is repeated over and over. You basically learn the same stuff, it could be cut in half.

the keras part was totally ignored