Build 2 models ( No. 1. Transfer learning- completed but to be adjusted and No.2. Enhanced CNN- to be done ) for comparison.

General instruction: You should do transfer learning first ( to mend code available on github -accuracy is 94% ) and then you should develop&train your enhanced CNN.

Then you should do a comparative analysis of both model.

you should catering for the below in your python codes?

-Application of pre-trained model

-Development of enhanced CNN

-Explanation on training set and testing set

-How you have trained the model and solve the issues of overfitting

-There are very little difference between different abnormalities. How you cater for that?

-What are the parameters that influence the performance

-Detailed description on performance and evaluation

-In medical field, GAN is being used instead of data augmentation

-What you do in case of many unlabeled data?

Reference No.1 for Transfer Learning model:

Tutorial: [login to view URL] watch from 33:15 to 43:15

Github code: [login to view URL]

Reference No.2 for Transfer Learning model:

Github code: [login to view URL]

Reference No.3 model:

Github code: [login to view URL]

Note: You can use google colab for the training of the 2 models.

Payment clause: 1. Payment will be done when both models have been developed and trained and full comparative analysis reports are done.


Навыки: Deep Learning, Machine Learning (ML), Python, Искусственный интеллект

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О работодателе:
( 6 отзыв(-а, -ов) ) Surinam, Mauritius

ID проекта: #31792975


(16 отзывов(-а))

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