Deep Learning for Computer Vision
summary
Convolutional nets are presented as practical tools not miracles: pretrained VGG/ResNet weights + fine‑tuning get fast, high‑accuracy results (96.3% on 3.5k clothing images), ImageNet top‑5 error drops to 2.25% vs human ~5%, and a 256‑dim embedding + nearest‑neighbors yields a visual‑similarity feature that prototypes in about an hour. Real systems include an image‑moderation pipeline that replaces manual reviewers and a production visual search that runs at scale predicting labels for millions of images per day.
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chapters (13)
- 0:00Intro
- 2:34Object detection
- 5:59Style Season Transfer
- 7:59What is a neuron?
- 8:34What is an Activation Function?
- 9:55What is a (Deep) Neural Network?
- 10:581 Layer Neural Network
- 12:58How does a neural network learn?
- 17:45What is a convolutional Neural Network?
- 19:54Example Architecture
- 25:21Convolution Learn Hierarchical features
- 29:16ImageNet Top 5 Error Rate
- 34:00Brief Error Analysis
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