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A couple of years ago I completed Deep Learning Specialization taught by AI pioneer Andrew Ng. I found this series of courses immensely helpful in my learning journey of deep learning. After years, I decided to prepare and share some notes which highlight key concepts I learned in this specialization.
The content of these documents is mainly adapted from this GitHub repository. I have added some explanations, illustrations, and visualization to make some complex concepts easier to grasp for readers. These could be good references for Machine Learning Engineers, Deep Learning Engineers, and Data Scientists to refresh their minds on the fundamentals of Deep Learning. You can download notes for each course from the following links. I’m going to prepare and share notes for all courses in this specialization in the near future.

Course 1: Neural Networks and Deep Learning

Coming soon…

Course 2: Improving Deep Neural Networks

Coming soon…

Course 3: Structuring Machine Learning Projects

Coming soon…

Course 4: Convolutional Neural Networks (CNNs)

This course teaches how to build convolutional neural networks and apply them to image data. When it comes to computer vision, CNNs are the bee knees. CNNs have given rise to incredible improvements in facial recognition, classifying X-ray reports, and self-driving car systems. Notes are based on lecture videos and supplementary material provided and my own understanding of the topics.

Course 5: Sequence Models

Coming soon…

Happy Learning!

Amir Masoud Sefidian
Amir Masoud Sefidian
Machine Learning Engineer

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