Week 1 of the plan was to train a feedforward network using the MNIST dataset. This is probably the easiest and most straightforward example to understand how the training cycle goes.
Before starting, I should mention that the code for this week -and the other weeks too- was not written from scratch by me. And that’s the main difference between school and work. It treated this homework the same way I treat work. I can google what I want and understand/refactor it. It can also be about reading about a specific network and understanding the recommended range of values for a specific parameter or the recommended layers structure.
This time, it was too simple that I copied the code and started playing with it to understand how it works. And as we move forward in the weeks, I had to write more myself. Consider it a Hello World week. Now lets see the code!
Before starting, I should mention that the code for this week -and the other weeks too- was not written from scratch by me. And that’s the main difference between school and work. It treated this homework the same way I treat work. I can google what I want and understand/refactor it. It can also be about reading about a specific network and understanding the recommended range of values for a specific parameter or the recommended layers structure.
This time, it was too simple that I copied the code and started playing with it to understand how it works. And as we move forward in the weeks, I had to write more myself. Consider it a Hello World week. Now lets see the code!
You can find the whole python notebook on Github here.
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