Image net classification with deep convolutional neural networks

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heedaeKwon

Engineering

Contents
1. Introduction
2. The dataset
3. Methods
4. result

Introduction
- Collect large date
=> =>
- Learn model powerfoul models
=> Methods
- Use better techniques
=> Methods

Method
- faster than sigmoid , tanH
ReLU

Method
GTX580 x 2
1.2millions
Local Response Normalization
Training on Multiple GPU

Method
256x256 => 224x224
Altering intensity RGB Channels
Data augmentation

Details of learning
Batch size = 128 exmples
Momentum = 0.9
Weight decay = 0.0005
Learning rate = 0.01

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