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The Deep Learning with Keras Workshop

You're reading from   The Deep Learning with Keras Workshop Learn how to define and train neural network models with just a few lines of code

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800562967
Length 496 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Table of Contents (11) Chapters Close

Preface
1. Introduction to Machine Learning with Keras 2. Machine Learning versus Deep Learning FREE CHAPTER 3. Deep Learning with Keras 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers 5. Improving Model Accuracy 6. Model Evaluation 7. Computer Vision with Convolutional Neural Networks 8. Transfer Learning and Pre-Trained Models 9. Sequential Modeling with Recurrent Neural Networks Appendix

4. Evaluating Your Model with Cross-Validation Using Keras Wrappers

Activity 4.01: Model Evaluation Using Cross-Validation for an Advanced Fibrosis Diagnosis Classifier

In this activity, we are going to use what we learned in this topic to train and evaluate a deep learning model using k-fold cross-validation. We will use the model that resulted in the best test error rate from the previous activity and the goal will be to compare the cross-validation error rate with the training set/test set approach error rate. The dataset we will use is the hepatitis C dataset, in which we will build a classification model to predict which patients get advanced fibrosis. Follow these steps to complete this activity:

  1. Load the dataset and print the number of records and features in the dataset, as well as the number of possible classes in the target dataset:
    # Load the dataset
    import pandas as pd
    X = pd.read_csv('../data/HCV_feats.csv')
    y = pd.read_csv('../data/HCV_target...
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