Transfer Learning
Let's talk about the fastest and easiest way you can build a deep learning model, without worrying too much about how much data you have.
Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07
References and further reading:
CS231n: Convolutional Neural Networks for Visual Recognition
http://cs231n.github.io/transfer-learning/
Best Practices for Fine-tuning Visual Classifiers to New Domains
http://adas.cvc.uab.es/task-cv2016/papers/0002.pdf
How transferable are features in deep neural networks?
https://arxiv.org/pdf/1411.1792.pdf
What makes ImageNet good for transfer learning?
https://arxiv.org/pdf/1608.08614.pdf
ImageNet
http://www.image-net.org/
How neural networks build up their understanding of images
https://distill.pub/2017/feature-visualization/
Distilling the Knowledge in a Neural Network
https://arxiv.org/pdf/1503.02531.pdf
#deeplearning #machinelearning
Let’s talk about the fastest and easiest way you can build a deep learning model, without worrying too much about how much data you have.
Deep Learning Crash Course playlist: https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07
References and further reading:
CS231n: Convolutional Neural Networks for Visual Recognition
http://cs231n.github.io/transfer-learning/
Best Practices for Fine-tuning Visual Classifiers to New Domains
http://adas.cvc.uab.es/task-cv2016/papers/0002.pdf
How transferable are features in deep neural networks?
https://arxiv.org/pdf/1411.1792.pdf
What makes ImageNet good for transfer learning?
https://arxiv.org/pdf/1608.08614.pdf
ImageNet
http://www.image-net.org/
How neural networks build up their understanding of images
https://distill.pub/2017/feature-visualization/
Distilling the Knowledge in a Neural Network
https://arxiv.org/pdf/1503.02531.pdf
#deeplearning #machinelearning
