The document summarizes research on using deep convolutional neural networks for image classification on the ImageNet dataset. It describes collecting a large dataset, using techniques like ReLU activation, local response normalization, overlapping pooling, dropout, and training across multiple GPUs. The results showed the CNN approach enabled more powerful models for image classification compared to previous methods.

1. ImageNet Classification with
Deep Convolutional Neural
Networks
Feb 13, 2021
HeeDae Kwon

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

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

4. The Dataset

5. Method
Overall structure

6. Method
- faster than sigmoid , tanH
ReLU

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

8. Method
• Overlapping Pooling

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

10. Method
Dropout

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

12. Results

13. • 감사합니다