What are Convolutional Neural Networks? ๐๏ธ โญ๏ธ โฐ๏ธ
CNNs are an important class of deep artificial neural networks that are particularly well suited for images.
If you want to learn the important concepts of CNNs and understand why they work so well, this thread is for you!
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What is a CNN? ๐ค
A CNN is a deep neural network that contains at least one convolutional layer. A typical CNN has a structure like this:
โช๏ธ Image as input
โช๏ธ Several convolutional layers
โช๏ธ Several interleaved pooling layers
โช๏ธ One/more fully connected layers
Example: AlexNet
A good example - AlexNet
Throughout the thread I will be giving examples based on AlexNet - this is the net architecture that arguably started the whole deep learning revolution in computer vision!
This is the formula for Mean Squared Error (MSE) as defined in WikiPedia. It represents a very simple concept, but may not be easy to read if you are just starting with ML.
Read below and it will be a piece of cake! ๐ฐ
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The core โซ
Let's unpack from the inside out. MSE calculates how close are your model's predictions ลถ to the ground truth labels Y. You want the error to go to 0.
If you are predicting house prices, the error could be the difference between the predicted and the actual price.
Why squared? 2๏ธโฃ
Subtracting the prediction from the label won't work. The error may be negative or positive, which is a problem when summing up samples.
You can take the absolute value or the square of the error. The square has the property that it punished bigger errors more.