Deep Learning - Crash Course 2023 - Training Evaluation

The video tutorial explains how to use the history object in Python to track training progress, visualize training and validation metrics, make predictions on test data, and evaluate model accuracy. It covers accessing the history object's attributes, plotting loss and accuracy, converting prediction outputs to binary, and calculating accuracy using the SKLN matrix. The session concludes with a preview of the next video, which will summarize the deep learning model-building process.

What information does the history object contain about the training process?

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How can you visualize the training progress in terms of loss and accuracy?

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Describe the process of making predictions on test data using the model.

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What steps are involved in converting the model's output to binary values?

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What is the significance of the accuracy score in evaluating the model's performance?

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