First off, I would just like to say that your blog posts are awesome. The math you are blogging about is extremely complicated (if you are trying to read it via papers) but your posts definitely explain it in Layman's terms better than anything I have come across.
I would change the wording this paragraph from:
"At its most basic, convolutional neural networks can be thought of as a kind of neural network that uses many identical copies of the same neuron.1 This allows the network to have lots of neurons and express computationally large models while keeping the number of actual parameters – the values describing how neurons behave – that need to be learned fairly small."
to something like this
"At its most basic, convolutional neural networks can be thought of as a kind of neural network that uses many identical copies of the same neuron.1 This allows the network to have lots of neurons and express computationally large models while keeping the number of actual parameters (the values describing how neurons behave) that have to be learned fairly small."
I had to read that last sentence a few times to figure out what was going on. This makes more sense to me. Keep up the good work!
First off, I would just like to say that your blog posts are awesome. The math you are blogging about is extremely complicated (if you are trying to read it via papers) but your posts definitely explain it in Layman's terms better than anything I have come across.
I would change the wording this paragraph from:
"At its most basic, convolutional neural networks can be thought of as a kind of neural network that uses many identical copies of the same neuron.1 This allows the network to have lots of neurons and express computationally large models while keeping the number of actual parameters – the values describing how neurons behave – that need to be learned fairly small."
to something like this
"At its most basic, convolutional neural networks can be thought of as a kind of neural network that uses many identical copies of the same neuron.1 This allows the network to have lots of neurons and express computationally large models while keeping the number of actual parameters (the values describing how neurons behave) that have to be learned fairly small."
I had to read that last sentence a few times to figure out what was going on. This makes more sense to me. Keep up the good work!