Deep Learning - Computer Vision for Beginners Using PyTorch - Preparation and Evaluation

The video tutorial covers setting up a machine learning model, focusing on batch size selection, data preparation, and model setup. It explains creating training and test datasets using torchvision, defining the model, loss function, and optimizer. The tutorial also includes a detailed explanation of the evaluation function to assess model performance. Finally, it demonstrates training the model over multiple epochs and printing the results after each epoch.

What is the chosen batch size for the training process?

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Explain how the training and test datasets are created in the code.

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What loss function is used for the multiclass classification problem?

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Describe the process of evaluating the model as outlined in the video.

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What steps are taken to fit the model to the data?

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How is the optimizer updated during the training process?

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What is the significance of printing the model performance after each epoch?

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