# CNN architectures and transfer learning

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/guide/8

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/guide/8/

1. [AlexNet and the reason for its components](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s0/)
2. [Inception examines several spatial scales](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s1/)
3. [Why one by one convolution saves work](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s2/)
4. [ResNet adds a shortcut](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s3/)
5. [Transfer learning from a small dataset](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s4/)
6. [Model answer: feature extraction and fine-tuning](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s5/)
7. [How to draw the architecture diagrams](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s6/)
8. [Inception concatenation and residual addition](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s7/)
9. [Model answer: AlexNet, Inception and ResNet](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s8/)
10. [Applications of convolutional neural networks](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s9/)
11. [Reading LeNet and AlexNet layer diagrams](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s10/)
12. [Top-1 and top-5 error: what an ImageNet result means](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s11/)
13. [VGG16: small filters in a regular sequence](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s12/)
14. [Global average pooling: one average for each channel](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s13/)
15. [The faculty's 480-channel bottleneck numerical](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s14/)
16. [Batch normalization inside a residual network](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s15/)
17. [Residual-block order and architecture qualifications](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s16/)
18. [Choosing frozen layers from dataset size and similarity](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s17/)
19. [Four ways to reuse a pretrained model](https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c8-s18/)