What made AlexNet (2012) a breakthrough?
easyAnswer
- AlexNet: 8 layers, ReLU activations (huge speedup vs sigmoid), dropout (novel regularization), overlapping max pooling, local response normalization (LRN, now obsolete), heavy data augmentation, and GPU training split across 2 GTX 580s.
- Won ImageNet 2012 by 10+ percentage points — kicked off the deep-learning revolution and made GPUs mandatory for vision.
Check yourself — multiple choice
- AlexNet used sigmoid + no dropout
- 8 layers with ReLU + dropout + GPU training + heavy augmentation — kicked off the DL era in vision
- Same as LeNet
- AlexNet had zero data augmentation
AlexNet: ReLU + dropout + GPU + augmentation → deep learning's ImageNet breakthrough.
#architectures#computer-vision
Practise Deep Learning
214 interview questions in this topic.