Jupyter Notebook, Tensorflow GPU 2.4.1, CUDA

The notebook has been shut down around 3 times a day,mostly due to problems in hardware, models are saved when need and loaded when needed, also the numbers of the cells final document is NOT the actual path of execution. The Notebook was not restarted at any time so the output of the cells can be kept.

1. The data is not included - 5.45 GB
2. There is no cached data - 115 GB (only indexes are kept)
3. The saved models are not included 19 models are 5.33 GB (best model CNN-16 315 MB)
4. The logs are not included 171 MB of data for 21 models.
5. The documet is done for about 290h of PC working time and 120h - 130h work done by me and I have to apologize for typos. Enjoy.

Content:

1. Introduction
2. Get Data
3. Pepair the data for modeling.
4. Hardware and Model Choosing.
   1. Hardware
   2. Model Choosing
5. Crating a Convolutional neural network model.
   1. CNN-1
   2. CNN-2
   3. CNN-3
   4. CNN-4
   5. CNN-5
   6. CNN-6
   7. CNN-7
   8. CNN-8
   9. CNN-9
6. Adding Test Dataset
7. Continue with Convolutional neural network model
   1. CNN-10
   2. CNN-11
   3. CNN-12
   4. CNN-13
   5. CNN-14
   6. CNN-15
   7. CNN-16
8. Impoving Training
9. Tuning the best models
   1. CNN-10
   2. CNN-15
   3. CNN-16
10. Tranfer Learning
    1. TL model 1
    2. TL model 2
    3. VGG19 TL model
       1. Create VGG19 transfer model
       2. Data VGG19 transfer model
       3. Train VGG19 transfer model
11. Use the base VGG-19 Model
12. Test he best model
13. Compare results
14. Conclusion
15. References