Deep Learning is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self–driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more.
Now you might be wondering :
There is a very large number of courses well–explaining deep learning, why should I prefer this specific course over them ?
The answer is : You shouldn’t ! Most of the other courses heavily focus on Programming deep learning applications as fast as possible, without giving detailed explanations on the underlying mathematical foundations that the field of deep learning was built upon. And this is exactly the gap that my course is designed to cover. It is designed to be used hand in hand with other programming courses, not to replace them.
Since this series is heavily mathematical, I will refer many many times during my explanations to sections from my own college level linear algebra course. In general, being quite familiar with linear algebra is a real prerequisite for this course.
Please have a look at the course syllables, and remember : This is only part (I) of the deep learning series!
Courses : 3
Specification: Deep Learning & Neural Nets Mathematical Derivations-Part-1
1 review for Deep Learning & Neural Nets Mathematical Derivations-Part-1