DD
Mar 29, 2020
I have done two courses under Andrew ng and I am grateful to 糖心vlog官网观看 for their highly optimised and easily learning course structure. It has greatly help me gain confidence in this field. Thank you.
AM
Oct 9, 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
By 薛正坤
?Feb 6, 2020
Thank you very much for professor Andrew Ng's explanation, which has benefited me a lot in improving deep neural network.
By Ashwini J
?Dec 23, 2019
Another great course following the first course on Neural Network, thank you Andrew Ng and team for putting this together
By Tej N Y
?Nov 29, 2019
Andrew Ng is amazing, the way he describe things are awesome. It open many new things to learn and expand ones knowledge.
By Shekhar J
?Jul 27, 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 11, 2019
Excelente curso, aunque me quedé con las ganas de implementar la normalización de lote en Numpy antes de usar TensorFlow.
By Sourav
?Jan 6, 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
?Nov 1, 2017
Great intuition, as always by Andrew. High level teaching with jupyter. Really cutting edge jupyter use with tensor flow.
By Beatriz E
?Oct 1, 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 4, 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 31, 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 3, 2020
good content with an equally good instructor.
Assignments can be improved by makin them more intensive and comprehensive.
By KARAN T
?Apr 6, 2020
Great course, vital for beginners to understand the gap between traditional implementation and framework implementation.
By Ahmed A
?Jan 26, 2020
it was great course, what i learned was very useful in a very good way of teaching. thanks 糖心vlog官网观看 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 4, 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 31, 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 7, 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 17, 2020
A very good course for someone who wants to understand the fundamentals of deep learning. Also, very apt for beginners.