MM
Jan 24, 2021
I already knew the subject, so I was able to go fast, but I really loved the completeness of this course, the approach, the tests, and the capstone project. Basically everything. Very good indeed!
AA
Mar 17, 2021
Provided clear and useful insight into TensorFlow 2. Before the course I had read many of the TF2 guides and tutorials. This course helped solidify my understanding of core TF concepts.
By José P
•Apr 18, 2021
Nice course, thank you all!
By Ragul N
•Nov 23, 2020
Best course on Tensorflow 2
By akshaykiranjose
•Jun 6, 2022
thanks, guys at imperial
By Dai T
•Sep 21, 2020
Best Tensorflow Course!
By Hugo R V A
•Sep 16, 2021
Just amazing! Perfect!
By Duc A L
•Feb 19, 2021
Good course to learn
By Mohammed A K
•Jan 25, 2022
outstanding course!
By Айрапетян Ж С
•Jan 28, 2021
Great intro to tf2.
By MoChuxian
•Oct 27, 2020
very nice course!!!
By Alireza K
•Aug 17, 2023
very good progects
By Nguyen T S
•Jan 24, 2022
Excellent course!
By Gustavo X A M
•Nov 18, 2020
Excellent course
By Wong H S
•Nov 2, 2020
Awesome content!
By Peter W
•Feb 24, 2021
great course!
By Yuzhe D
•Dec 28, 2020
Great course
By Engr. M S K
•Mar 7, 2022
Best Course
By Adam H
•Nov 29, 2022
Amazing!
By Meng O L
•Oct 27, 2020
Awesome
By Fayas A
•Jun 18, 2023
great
By Kellen X
•Dec 11, 2020
Great
By Yukihiro F
•Nov 1, 2023
First of all, this course is not a deep learning course but a TensorFlow2 course, so prior knowledge of deep learning is required. For example, if you don't understand what CNN (Convolutional Neural Network) is, it's recommended to take a dedicated deep learning course, such as OpenAI's, before starting this course. With that in mind, the content of this course is excellent. You will become proficient in executing what you imagine using TensorFlow2. However, there are some drawbacks. Almost every module consists of the following components: 1. Videos where the instructor explains concepts while showing code. 2. Videos where the TA (Teaching Assistant) explains concepts while coding. 3. JupyterNotebook exercises. 1 and 2 are quite redundant. Moreover, video 2 involves typing out code from scratch instead of explaining pre-written code, which makes the videos unnecessarily long. Typing out code while watching the video was quite painful. In addition, the lab's automatic grading tool was a bit unstable. Particularly, during the second week's auto-grading, it would fail when the lab was working correctly, and pass when errors occurred while coding as per the instructions on the lab. Additionally, there were issues with the peer review assignment, as attempting to generate a PDF as instructed resulted in a server-side error, preventing the output. I downloaded the Jupyter project and struggled to generate a PDF from my local JupyterLab.
By J H v d M
•Apr 24, 2021
If it were not for the difficulties encountered with using some of the online Labs, esp. the Capstone project, would have been 5 stars (which I rarely give).
I had to resort to using my own 2016 vintage Asus laptop w. 1070 GPU to get the Capstone going; the Lab totally gummed up.
All in all excellent course also as refresher. On to completing the next two now.
Thanks a lot, Jan van de Mortel
By Marcelo B
•Apr 10, 2021
The course is a good introduction to applicable deep learning using Keras. Do not expect any mathematical derivations. These you would need to gather from other courses such as Deeplearning.ai. I enjoyed the quizzes. The capstone project could be made more challenging by exploiting different aspects of layers, benchmarking pre-trained networks, and training strategies.
By Christian C
•Aug 24, 2020
For me, the course lived up to its name. It was not too theory-focused, and focused really on getting started with TensorFlow 2. The programming exercises, which use different well-known datasets, are designed just enough to not feel being spoon-fed. The peer-reviewed capstone project is also a great experience.
By Daniel H
•Apr 1, 2021
A clearly organized and presented course. The capstone project may be a bit challenging because you will need to recall what you learned and apply it to what you may already know or need to go research. It's worth the effort.