State of the Art Convolutional Neural Networks (CNNs) Explained | Deep Learning in 2020

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I introduce what a convolutional neural network is and explain one of the best and most used state-of-the-art CNN architecture in 2020: DenseNet. If you would like me to cover any other neural network architecture or research paper, please let me know in the comments!

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Chapters:
0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise.
0:18 The Convolutional Neural Networks
0:39 A … convolution?
2:07 Training a CNN
2:45 The activation function: ReLU
3:20 The pooling layers: Max-Pooling
4:05 The fully-connected layers
4:40 The state-of-the-art CNNs: A quick history
5:23 The most promising CNN architecture: DenseNet
8:39 Conclusion

#CNN #DenseNet #DeepLearning

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I like how you are not forcing excitement on people like the two minute papers do

xgxfhzxfuhfjgfhgf

when you say , ofc its the most basic example in the end 💀, the whole video was really fun to watch, i started out with the fundamentals watching your how to get started with ai, and im on your discord as well, hope i am able to be consistent, it was a great video thank you for it

introxart

Material quality has room for growth! Good luck;)

neworldemancer

I think it would be interesting to cover Xception nets, since depthwise separable convolutions seem super promising at reducing network parameters while maintaing capacity. And covering Xception will also introduce concepts used in other architectures like mobile nets, resnets and efficient nets!

LX

Thanks for this video you trimmed down CNN to basic! Great job!

Btw can you make YOLO feedback as input to improve YOLO result???

yt-sh

Thanks, Kindly cover different models architecture as well.

mrahmadafaq

Amazing video! Could you please make a video to explain apply transfer learning on some of the popular networks?

jiaduan

Can you make a comparison between CNN and Graph Convolution Neural Network architecture? How are these 2 differ?

deyvitt

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