This is my note for the course (Convolutional Neural Networks). The codes in this note are rewritten to be more clear and concise.

👉 Course 1Neural Networks and Deep Learning.
👉 Course 2Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.
👉 Course 3Structuring Machine Learning Projects.
👉 Course 5Sequence Models.

This course will teach you how to build convolutional neural networks and apply it to image data.

Computer vision

• Challenges of computer vision: inputs get really big. $\Rightarrow$ Easily lead to overfitting and infeasible!

Edge Detection

• We wanna detect vertical / horizontal edges. $*$ is called a convolution operator. The $3\times 3$ square is called a filter (kernel)

Horizontal edge detection.ref

Vertical vs Horizontal.ref

• There are many different filers we could use. Sobel filter and Scharr filter ‘s advantage is that it allows you to put a little bit more weight to the central row of the central pixel, this makes it maybe a little bit more robust.

Sobel filter and Scharr filter.ref

References

👉 Course 5Sequence Models.

Notes with this notation aren't good enough. They are being updated. If you can see this, you are so smart. ;)