Week 4: From Theory to Training - My First Neural Networks

Week 4 done. Last week: Shallow algorithms (Linear Regression, Logistic Regression). This week: Neural networks - actually building and training them. Still not LLMs. Still not ChatGPT integrations. Still "boring" ML. But here's why: I want to understand what's actually happening, not just call APIs. The difference? Last week I learned what models predict. This week I learned how they learn. The Shift: From Equations to Architectures This week was about understanding when complexity is worth it. What I Actually Built 1. Handwritten Digit Recognition (MNIST) The problem: Recognize handwritten digits (0-9) from 28×28 pixel images. import torch import torch.nn as nn class DigitClassifier ( nn . Module ): def __init__ ( self ): super (). __init__ () self . layers = nn . Sequential ( nn . Flatten (), nn . Linear ( 784 , 128 ), nn . ReLU (), nn . Dropout ( 0.2 ), nn . Linear ( 128 , 64 ), nn . ReLU (), nn . Dropout ( 0.2 ), nn . Linear ( 64 , 10 ) ) def forward ( self , x ): return self . la