Paper #2: What AlexNet Learned, and Why It Mattered
With AlexNet trained, this last post looks at what the network learned, and why this paper mattered.
Tag
computer vision basics
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: 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 #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 #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 3: The Simplest Way to Guess (Nearest Neighbor)
Before we build anything clever, let's try the dumbest possible approach to image classification. It's simpler than it sounds, and it teaches us something crucial about why this problem is hard.
Paper #1, Part 2: The Problem of Classification
Given a grid of numbers, name what the picture shows. It sounds simple. Seven specific things make it one of the hardest problems computers ever faced.
Paper #1, Part 1: What Does It Mean for a Computer to 'See' a Picture?
Before we build anything, we need one idea: to a computer, a picture is a grid of numbers. A small cartoon dog, saved as a text file, shows this directly. Every one of its numbers is readable.