Learner Reviews & Feedback for Introduction to Deep Learning & Neural Networks with Keras by IBM
About the Course
many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural
networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library. After
completing this course, learners will be able to: • Describe what a neural network is, what a deep learning model is, and the difference between
them. • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzman...
Top reviews
SS
Jun 29, 2020
Such a wonderful and high tech course in the world and it is provided by ibm and coursera.Thank you ibm and coursera for such a opportunity.I'm glad and proud to be a part of this organization.
MP
Jun 30, 2022
Excellent introduction to the mechanics of Neural Networks in general, and the Keras application specifically. Alec is an outstanding teacher, I always appreciate his knowledge and enthusiasm.
251 - 275 of 340 Reviews for Introduction to Deep Learning & Neural Networks with Keras
By Lateefat O B
•Apr 7, 2024
wonderful
By Abul B
•Mar 10, 2022
Excellent
By Sambit S
•Sep 1, 2021
very good
By Dr C S Y
•Aug 22, 2021
Excellent
By Souvik M
•Apr 21, 2020
Excellent
By Saman S
•Sep 25, 2019
wonderful
By Mohamed
•Feb 11, 2025
its good
By Ridha O
•Feb 11, 2022
good one
By SIMHA C J
•Aug 26, 2024
Awesome
By José M
•Mar 27, 2023
Good!!
By parisa z
•Nov 9, 2022
great
By Francisco M L L
•Aug 8, 2022
great
By said f
•Mar 29, 2020
super
By Nithya P V 2
•Mar 28, 2025
dfdf
By Ahmed E
•Aug 15, 2024
good
By Sardor B
•May 22, 2024
good
By Astitva S
•Mar 18, 2024
good
By 01fe21bec413
•Mar 16, 2024
Good
By mezmur w
•Mar 6, 2024
best
By afra a a
•Dec 21, 2023
good
By Muhammad M T
•Mar 22, 2023
good
By Krishna H
•Apr 29, 2020
good
By Gorana B
•Jul 22, 2024
It is short and comprehensive introduction. It could have had a dedicated module on evaluation of the models, with visualizations of target vs predictions and losses. From evaluation of peer-graded assignments I get the impression this is not well understood (ways to do it, meaning of values vs training and epochs). On the other hand peer graded assignment should be more challenging than what is shown throughout the course. So maybe it is enough what was shown throughput the course, as current assignment is a bit more challenging. Otherwise students end up copy pasting materials (which I have seen too often). My problem is more on the concept of evaluation of the assignment and points to be given. Scale is too coarse. And submission request should be less loose - jupyter notebook or python files, not html or pdf files. And some system that is automatically checking for similarities among student's assignments prior to submission would be good to have.
By Rafael G
•Nov 3, 2021
Very good course which gives a good introduction to the field. Don't get intimidated by the math you will see and make sure you understand the workflow. Once you do that you will basically repeat it in which one of the neural network types presented at the course. In a negative not, I missed the intructor elaboring how to identity problems that could be approached by applying DL. But I complemented studies on other documents in the internet and that's ok.
By Michael M
•Apr 14, 2020
It was a pretty good brief, rapid intro. I frankly was expecting more content on options and explanations, but it covered the very essential basics. The final exercise did ask for students to use tools not gone over in class (a bit of scikit-learn). Since I've used scikit-learn before, this wasn't hard for me, but it may be for a newcomer, and actually isn't needed to meet the goals of the assignment, so I'm not sure why it was there.