This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus. Although the prerequisite is minimal, we will discuss many advanced topics including:
1) functions and their computational graphs.
2) neural networks
3) conceptually understand the derivative and the gradient.
4) gradient descent and backpropagation
5) the multivariable chain rule
6) mini–batch gradient descent
Instructor Details
Courses : 2
Specification: Image Recognition with Neural Networks From Scratch
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10 reviews for Image Recognition with Neural Networks From Scratch
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Price | $14.99 |
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Provider | |
Duration | 3 hours |
Year | 2020 |
Level | All |
Language | English |
Certificate | Yes |
Quizzes | No |
$59.99 $14.99
Gawe Jakimiak –
Perfect intro for Image Recognition
Joshua Beach –
very useful, would really recommend
Debajyoti Saha –
It is a decent course
Gyorgy Simko –
Clear, easy to understand math based introduction
Abhinav Reddy –
No, it was terrible, I expected to have a CLEAR and EASY way to follow the course. Although this person knows what they are doing, they cant teach very well. How can I create a machine learning program if he won’t even share the databases with me
Vasil Stoyanov Atanasov –
Not much practical value, math behind gradient descent is explained in details.
Mounish Krishna –
voice is not clear
Yuyu Chen –
This course is amazing so far! I’m over half way done with the course. Dr. Long delineated the content so nicely. The contents are practical, concise, and manageable. Although there is so much for me to digest, I feel like I am getting more and more each time I go over the video and by doing the homework assignments. So far, this is course has been great! I highly recommend it!
Pranav Hari –
Yes, it is interesting so far. Can try to be more engaging. Knows material well and homeworks also help.
Shannon Russell –
Enjoyable! Teaches math behind neural nets!