AM
Oct 8, 2019
I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation
JS
Apr 4, 2021
Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.
By Shekhar J
•Jul 26, 2019
This course is very good for basics and to start learning frameworks such as Tensorflow. I enjoyed learning this course .
By Dunitt M
•Jan 10, 2019
Excelente curso, aunque me quedé con las ganas de implementar la normalización de lote en Numpy antes de usar TensorFlow.
By Sourav
•Jan 5, 2019
Learnt a great deal about tuning models. Concepts of regularization, batch norm and optimizers were very well explained.
By Subham K
•Jan 5, 2019
It was so awesome .I got to know the minute details which would certainly help me in making a better deep learning model.
By Dongxiao H
•Dec 5, 2017
This coursera really tells me a lot about how to tune parameters, and very useful skills to optimize the NN. Very thanks!
By george v
•Oct 31, 2017
Great intuition, as always by Andrew. High level teaching with jupyter. Really cutting edge jupyter use with tensor flow.
By Beatriz E
•Sep 30, 2017
Very good course, including the intro to Tensorflow. Highly recommended. I look forward to the next course in the series.
By Manish L
•Sep 8, 2017
Excellent coverage of key concepts and applied knowledge!! Thanks a lot to Prof Andrew and everybody in the course team!!
By Ezra S
•Sep 3, 2017
Andrew Ng's MOOCs are a *cut above* almost everyone else's. & I've finished over a dozen MOOCs on a variety of platforms.
By Olaf M
•Oct 30, 2023
Almost perfect. It would be better to correct the math typos by recording some videos again instead of adding the errata
By Dulan J
•Jul 4, 2021
This course is really good. I got a good understanding about the Hyperparameter Tuning, Regularization and Optimization.
By Harit J
•May 2, 2020
good content with an equally good instructor.
Assignments can be improved by makin them more intensive and comprehensive.
By KARAN T
•Apr 5, 2020
Great course, vital for beginners to understand the gap between traditional implementation and framework implementation.
By Ahmed A
•Jan 25, 2020
it was great course, what i learned was very useful in a very good way of teaching. thanks Coursera and thanks Andrew Ng
By Gabriel L
•Feb 20, 2019
So much practical knowledge packed in 3 weeks of study. Amazing tour de force on the practical aspects of deep learning!
By Onkar M
•Aug 1, 2018
Great course, but I have a suggestion, in that, more material related to Tensorflow should have been a great experience.
By Simeon B
•Jun 13, 2018
This course proved very helpful when I was grasping the ideas of hyperparameter search, regularization and optimization.
By Huanglei P
•Jun 3, 2018
I would say that I really enjoy taking the course led by Dr. Ng! Everything is explained in a clear and instructive way.
By Jorge R C
•Jan 30, 2018
Thanks for this amazing course, I learnt a lot about Deep Learning specifically Regularization and Optimization methods.
By Chuong N
•Nov 27, 2017
One of the best online course I have got! The way Prof Andrew conveys his ideas is exceptional! Totally love this class!
By Thapanun S
•Sep 6, 2020
Very good course that show you some insight on thing that it would take a lot of time if you experience it by yourself.
By Jagruti P
•Aug 16, 2020
A very good course for someone who wants to understand the fundamentals of deep learning. Also, very apt for beginners.
By Harsha V
•May 21, 2020
Nice course for beginners in Deep learning and a good introduction was provided for deep learning framework TensorFlow.
By Abderrazak C
•Apr 14, 2020
Ce cours est une étape très intéressant dans cette spécialisation. Je le conseille vivement aux apprentis. Bon lecture.
By Zeng X
•Apr 6, 2020
It really help solve a lot of question about how to improve dnn. Through this course, I learn basic knowledge about tf.