Prerequisites: Basic Python and Math

What You'll Learn

• Neural Network Architecture

  Describe the components of a neural network, including neurons, layers, weights, and biases.

• Activation Functions

  Explain the purpose and characteristics of common activation functions like Sigmoid, Tanh, and ReLU.

• Data Preparation

  Understand techniques for preparing data for neural network training, including scaling and encoding.

• Forward Propagation

  Implement the forward pass calculation to generate predictions from a neural network.

• Backpropagation

  Explain the process of backpropagation for calculating gradients.

• Gradient Descent

  Apply gradient descent and its variants to update network weights and minimize loss.

• Model Training

  Implement a basic neural network training loop.

• Regularization

  Understand common techniques like L1/L2 regularization and dropout to prevent overfitting.