作者：Adit Deshpande
Part1: A Beginner’s Guide To Understanding Convolutional Neural Networks
Part2: A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2
Part3: The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
编者按：比较完善的CNN入门文档，第一部分介绍CNN基本概念，第二部分介绍CNN相关的技术细节，比如：Pooling、ReLU、Dropout等，第三部分CNN、R-CNN系列、GAN等经典的9篇论文。

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Part1目录

1. Introduction
2. The Problem Space
3. Inputs and Outputs
4. What We Want the Computer to Do
5. Biological Connection
6. Structure
7. First Layer – Math Part
8. First Layer – High Level Perspective
9. Going Deeper Through the Network
10. Fully Connected Layer
11. Training (AKA:What Makes this Stuff Work)
12. Testing
13. How Companies Use CNNs

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Part2目录

1. Introduction
2. Stride and Padding
3. Choosing Hyperparameters
4. ReLU (Rectified Linear Units) Layers
5. Pooling Layers
6. Dropout Layers
7. Network in Network Layers
8. Classification, Localization, Detection, Segmentation
9. Transfer Learning
10. Data Augmentation Techniques

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Part3目录

1. AlexNet (2012)
2. ZF Net (2013)
3. VGG Net (2014)
4. GoogLeNet (2015)
5. Microsoft ResNet (2015)
6. Region Based CNNs (R-CNN – 2013, Fast R-CNN – 2015, Faster R-CNN – 2015)
7. Generative Adversarial Networks (2014)
8. Generating Image Descriptions (2014)
9. Spatial Transformer Networks (2015)