Fundamentals of Machine Learning - Multilayer Perceptron (MLP)

The video tutorial introduces neural networks, focusing on the multilayer perceptron (MLP) model using TensorFlow. It covers the MNIST dataset, data preprocessing, and building a neural network model with layers like flatten and dense. The tutorial explains training, evaluating, and saving the model, and introduces TensorBoard for performance visualization. Key concepts include data normalization, model architecture, and using confusion matrices for evaluation.

What is the significance of normalizing data before training a neural network?

Describe the process of compiling a neural network model.

What is the purpose of using a validation dataset during training?

How can TensorBoard be utilized in the context of machine learning?