Python for Deep Learning - Build Neural Networks in Python - Building the CNN Model

The video tutorial covers loading and reshaping the MNIST dataset, normalizing pixel values, and building a convolutional neural network (CNN) model. It explains the process of defining the model with convolutional and pooling layers, compiling it using sparse categorical cross entropy, and fitting the model to the dataset. The tutorial provides a step-by-step guide to preparing data and constructing a CNN for image classification tasks.

What does the term 'grayscale' imply in the context of image data?

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What are the dimensions of the input images used in the model?

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What is the difference between sparse categorical cross entropy and binary cross entropy?

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Why is it important to specify the number of epochs when fitting a model?

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