VB
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This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.
SV
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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.
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.
By Niel R
•Sep 5, 2017
Great introduction to neural networks. Gives both the mathematical basis and leads one though a practical implementation.