SV
Aug 29, 2018
Nothing can get better than this course from Professor Andrew Ng. A must for every Data science enthusiast. Gets you up to speed right from the fundamentals. Thanks a lot for Prof Andrew and his team.
BC
Dec 3, 2018
Extremely helpful review of the basics, rooted in mathematics, but not overly cumbersome. Very clear, and example coding exercises greatly improved my understanding of the importance of vectorization.
By Dinesh K K
•Nov 10, 2019
course gives an understand of the structure of the DLL image classification model. More than that is a very basic course.
By Calum
•Aug 23, 2019
Andrew clearly explains the material step by step, giving you a solid understanding of Neural Networks and Deep Learning.
By Kai-Chieh C
•May 13, 2019
course are gread. One suggestion, can the ppt slides merge into one file? it is quite annoying to download it one by one.
By Jon S
•Apr 26, 2019
Great primer on neural networks, learn backprop and other fundmental NN things by building NN's from scratch using numpy.
By Rajat M
•Apr 26, 2019
To switch from conventional Machine Learning to Deep Learning, this course is the foundation. Highly recommended course !
By Vivek P
•Mar 28, 2019
I want to know why we use the sigmoid function. I am always confused about how we got the formula to get the probability.
By VANGALA R T
•Mar 26, 2019
exceptionally great course....thank u so much for andrew ng sir and his team for wonderfull assignments and course work.
By Boris D
•Mar 4, 2019
Excellent course to understand the finest details of neural networks. Much better than the course from "Machine Learning"
By Kunnan L
•Feb 1, 2019
Have a basic idea of neural network and deep learning, and some programming experience of the image classifying project.
By Mohammad M H
•Jan 31, 2019
It's a great Course.Professor Andrew NG explains everything really easy.Learned a lot from this course.Thank you Coursera
By Dale J d C
•Jan 4, 2019
Concise and straight to the point. The important concepts are repeated and explained well even in a short amount of time.
By prasoon g
•Dec 6, 2018
Very good on fundamentals on how neural nets work. The equations and notations really helped to understand the algorithm.
By Rubén C C
•Oct 23, 2018
It was as I expected challenge enough to keep me on track and fairly complete. Andrew was very clear on his explanations.
By Shashank R
•Aug 30, 2018
Awesome step by step course to see the inner working of a deep neural network and hands on programming a NN from scratch!
By Arthur B
•Jul 6, 2018
Difficult concepts were explained very well, and programming assignments helped me understand core concepts being taught.
By James G
•Jul 2, 2018
Those mathematical symbols looks quite scary before I took this course, not they have become my toys, pretty cool course.
By Darien S
•Jun 14, 2018
Succinct and thorough at the same time.Very useful fundamentals. Especially when taken after the original 2011 ML course.
By Mohamed A
•Apr 27, 2018
You don't need to question a material that Prof Andrew has put together. Thanks very much for such an amazing experience.
By Andrew G
•Jan 18, 2018
Great course to get your hands dirty with the practical side of Machine Learning as well as a good theoretical basis too.
By Tiancheng X
•Nov 26, 2017
Really deepened my understanding on neural networks, especially the part about backward propagation. Great course indeed!
By gaurav t
•Oct 2, 2017
Father of deep learning, spreading knowledge in very elegant manner. It's very pleasureful, to be a troop of his AI army.
By Mayank
•Oct 2, 2017
The professor explained everything with great clarity and detail which was very useful in understanding concepts quickly.
By Gordon M
•Oct 2, 2017
Pretty great intro material. Very straightforward and definitely good for a quick refresher or introduction to the field.
By Deepak S
•Sep 18, 2017
Very Interesting course. Andrew is remarkably explain the great insight to neural network. Kudos to deeplearning.ai team.
By Nathan K
•Sep 10, 2017
Brilliant, well paced and easy to get into. You'll be stretched mentally to complete the course, but that's a good thing.