Deep Learning - Crash Course 2023 - Summary-2

The video tutorial covers the creation of a neural network using TensorFlow. It begins with data preparation, including train-test split, followed by model creation where neurons, layers, and activation functions are specified. The model is then compiled with an optimizer and loss function. After fitting the model, its performance is evaluated using test data, achieving an accuracy of 88%. The tutorial concludes with a summary and a preview of advanced techniques in deep learning.

What are the two parts into which the data is split during the data preparation phase?

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What is the purpose of specifying the number of neurons in each layer of the neural network?

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What activation function was used in the output layer of the neural network?

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Describe the process that follows after defining the model architecture.

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What was the accuracy achieved on the test data after performing predictions?

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