I Built a Neural Network from Scratch (No Frameworks) - Here's What I Discovered

I Built a Neural Network from Scratch - Here's What I Discovered I'm on a 42-week mission to deeply understand AI - from first principles to frontier research. No shortcuts. No model.fit() . Just raw Python and NumPy. Week 1: Build a neural network from scratch that recognizes handwritten digits. Here's what happened. The Setup Dataset : MNIST (60k training, 10k test images of digits 0-9) Architecture : Input (784) → Hidden (128) → Output (10) Tools : Python, NumPy, Hugging Face datasets Framework : None. That's the point. What Even Is an Input Layer? My first question wasn't about gradients or loss functions. It was: what information can you extract from a single pixel? I pulled the dataset from Hugging Face and went into the data itself before writing any code. The images are 28×28 pixels, and a pixel is just a number from 0 to 255 representing brightness. Nothing more. Flatten that grid and you get a vector of 784 numbers - that's your input layer. Not magic, just the data. From the