Deep Learning - Deep Neural Network for Beginners Using Python - Final Project Part 5

The video tutorial guides viewers through setting up a neural network model, defining its layers, and configuring training parameters such as learning rate and epochs. It addresses common errors encountered during model training and provides solutions for debugging. The tutorial concludes with evaluating the model's performance on test data, achieving high accuracy, and offers insights on modifying the network's structure. The video aims to equip viewers with the skills to implement and troubleshoot their own deep learning algorithms.

What is the method to determine the number of features in the dataset?

How many neurons are in the second hidden layer?

What learning rate was chosen for the model?

What issue was encountered regarding the weight matrices?

What was the training accuracy at the last epoch?

What was the final testing accuracy achieved by the model?

How can one change the structure of the deep neural network?