Who is this class for: Prerequisites: Expected: - Programming: Basic Python programming skills, with the capability to work effectively with data structures. Recommended: - Mathematics: Matrix vector operations and notation. - Machine Learning: Understanding how to frame a machine learning problem, including how data is represented will be beneficial. If you have taken my Machine Learning Course here, you have much more than the needed level of knowledge.

Created by:  deeplearning.ai

  • Andrew Ng

    Taught by:  Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Basic Info
Course 1 of 5 in the Deep Learning Specialization
Commitment4 weeks of study, 3-6 hours a week
How To PassPass all graded assignments to complete the course.
User Ratings
4.9 stars
Average User Rating 4.9See what learners said

How It Works

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
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Ratings and Reviews
Rated 4.9 out of 5 of 10,545 ratings

As usual, Professor Andrew Ng was awesome! He really took the time to explain what's going on in terms of the concept, which is great. He even went further to clarify the calculus behind the back propagation and how the derivatives of dW, db, dZ, and dA where determined. For me, calculus is not a problem, but for others who are not familiar with calculus and its chain rule, his detailed explanation would help. It shows how much effort Professor Ng is putting into his teaching of this course.

This is my second course on Coursera with Prof. Ng. First one was Introduction to Machine Learning. I look forward to taking more coursed offered by Professor Andrew Ng in future.


Fantastic course. I got a much better understanding of backpropagation in deep networks

It was perfact for people who are not familiar with mathtics and programming languages,

Very good intro into Deep Learning. Would be nice if there could be a special on calculus used for DL