Tutorial 91 - Building your first deep learning model - Breast cancer diagnosis
Notebook created in this video can be accessed at:
https://colab.research.google.com/drive/1WEZxybgoxQz8Lmp_r6Zq6OHYdvwaz2Df?usp=sharing
Problem statement:
Diagnose whether the patient has breast cancer using the features (attributes) provided.
What data is available?
Features (attributes) and corresponding labels (diagnosis) as a csv file.
https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)
OR
https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass
2 class problem: B-benign or M-malignant.
Strategy: Use deep learning to train a model using features as input and labeled diagnosis (B or M) as output on the training data. Then, evaluate the accuracy on the testing data.
Видео Tutorial 91 - Building your first deep learning model - Breast cancer diagnosis канала Apeer_micro
https://colab.research.google.com/drive/1WEZxybgoxQz8Lmp_r6Zq6OHYdvwaz2Df?usp=sharing
Problem statement:
Diagnose whether the patient has breast cancer using the features (attributes) provided.
What data is available?
Features (attributes) and corresponding labels (diagnosis) as a csv file.
https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)
OR
https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass
2 class problem: B-benign or M-malignant.
Strategy: Use deep learning to train a model using features as input and labeled diagnosis (B or M) as output on the training data. Then, evaluate the accuracy on the testing data.
Видео Tutorial 91 - Building your first deep learning model - Breast cancer diagnosis канала Apeer_micro
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