Paper #1, Part 10: Garbage In, Garbage Out (Why Data Prep Matters)
Before a network can learn from pixels, those pixels need to be put on a consistent scale. It's a small step that makes training much more stable.
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45 posts, newest first. Page 2 of 5.
Paper #1, Part 11: Memorizing vs. Learning (Overfitting and Regularization)
A network can memorize training data perfectly. But memorized answers don't generalize. Overfitting is the enemy, and regularization is how you fight it.
Paper #1, Part 12: Convolutions (Spotting Patterns Anywhere in a Picture)
A fully-connected network treats every pixel equally. Convolutions exploit one fact: important patterns are local. So a convolution scans the image in small patches, looking for a pattern wherever it appears.
Paper #1, Part 13: Pooling and Stacking (From Edges to Objects)
Convolutions find local patterns. Pooling zooms out. Stack them together and you build a hierarchy: edges → textures → shapes → objects.
Paper #1, Part 14: Peeking Inside the Black Box (What Is a CNN Looking At?)
Neural networks learn patterns, but we can't see them directly. Visualization techniques let us peek inside and understand what features a network has learned.
Paper #1, Part 15: Why Train From Scratch? Transfer Learning
Training a large CNN from scratch requires massive amounts of data and compute. Transfer learning reuses a network trained on one task as a head start on another.
Paper #1, Part 16: Putting It Together (What CS231n Actually Teaches)
Fifteen concepts. One goal: from raw pixels to classification. This is how it all fits together, and why CS231n became foundational reading.
Paper #2: The Contest That Changed Everything
Before 2012, teaching a computer to recognize objects meant designing the clues by hand. Then a deep neural network won the hardest contest in the field, and not by a little.
Paper #2: The Architecture (Everything from Paper #1, Scaled Up)
AlexNet didn't invent anything new in its shape. It's the convolutional network from Paper #1, made big: five convolutional layers, three fully-connected ones, and sixty million weights.
Paper #2: ReLU, the Small Change That Made Big Networks Trainable
AlexNet's shape is familiar from Paper #1. What we skipped is how the activation function controls training speed. Picking a fast one, ReLU, is the first thing that made training a network as big as AlexNet possible.