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These topics will be covered in this module’s knowledge check and entry ticket.

• Understand the basic structure and components of a feedforward neural network, including input layer, hidden layers, and output layer.
• Explain the purpose of training a neural network and the role of weights in determining the network’s predictions.
• Understand the roll of the loss function in training a neural network.
• Understand the role of gradient descent in training a neural network.
• Understand the concept of learning rate and its impact on the training process.
• Define an epoch in the context of neural network training.
• Understand the concept of batch size and its impact on training.
• Explain the purpose of early stopping and how it helps prevent overfitting.

• Neural networks: representation. (jeremyjordan.me)

NOTE: Don’t worry too much about the math in these videos! The goal is to get a general understanding and some intuition for how these work. A high level understand of the role each of these terms play is the goal.

TensorFlow Playground

Complete all section of the Learn Intro to Deep Learning course on Kaggle (est. 3-4 hours). Submit your completed file for the final exercise (Binary Classification) to the assignment in canvas.