Key Deep Learning Architectures for Visual Object Recognition
1 min readOct 1, 2018
This series provides overview of some of the prominent neural network architectures. Reading through this guide and all supplement materials will help you develop understanding of the modern neural network architectures and main ideas behind them.
Before starting, you should have some familiarity with the basics of neural networks, backpropagation algorithm and gradient descent. To learn these, I recommend two amazing courses:
- “Practical Deep Learning For Coders" from fast.ai
- “CS231n: Convolutional Neural Networks for Visual Recognition" from Stanford
List of architectures in this guide
Sorted in chronological order.
Coming soon:
- ZFNet (2013)
- GoogLeNet (2014)
- Inception (2014)
- VGG (2014)
- InceptionV2, InceptionV3 (2015)
- ResNet (2015)
- InceptionV4 (2016)
- DenseNet (2016)
- Xception (2016)
- MobileNet (2017)
- NASNet (2017)
- SE-ResNet (2017)
- MobileNetV2 (2018)
You can also follow my repository on GitHub that has the same content and will be continuously updated.
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