Supervised Learning - Classification Week 9 Challenge

1. What Is a Neural Network?

A neural network is a computer model inspired by the human brain, consisting of layers of nodes (neurons) that work together to learn patterns from data.

2. What Is Deep Learning?

Deep learning is a type of machine learning that uses neural networks with many layers (deep networks) to learn complex patterns and solve difficult tasks like image recognition or language processing.

3. Role of the Activation Function

An activation function decides if a neuron should be activated or not. It helps the network to learn non-linear relationships in data, making the model more powerful.

4. Different Types of Activation Functions

• Sigmoid: Outputs values between 0 and 1.
• ReLU (Rectified Linear Unit): Outputs 0 for negative values and the input itself for positive values.
• Tanh: Outputs values between -1 and 1.
• Softmax: Used for multi-class classification, gives probabilities for each class.

5. What Is Backpropagation?

Backpropagation is the process of adjusting weights in a neural network by calculating the gradient of the loss function with respect to each weight, working backward from the output to the input to improve the model's predictions.