Neural networks are a fundamental building block of deep learning. They are inspired by the structure and function of the human brain, and they enable computers to learn from data.

Introduction

A neural network consists of layers of interconnected nodes, or neurons. Each neuron takes input, applies a function to it, and passes the output to the next layer. This process is repeated until the final output is produced.

Types of Layers

  • Input Layer: Receives input data.
  • Hidden Layers: Apply transformations to the input data.
  • Output Layer: Produces the final output.

Activation Functions

Activation functions introduce non-linearity into the neural network, allowing it to learn complex patterns.

  • Sigmoid: Maps input to a value between 0 and 1.
  • ReLU (Rectified Linear Unit): Maps input to 0 if negative, and to its own value otherwise.
  • Tanh (Hyperbolic Tangent): Maps input to a value between -1 and 1.

Training a Neural Network

Training a neural network involves adjusting the weights and biases of the neurons to minimize the error between the predicted output and the actual output.

Loss Functions

  • Mean Squared Error (MSE): Measures the average squared difference between the predicted and actual outputs.
  • Categorical Cross-Entropy: Used for multi-class classification tasks.

Example

Suppose you want to classify images into cats and dogs. You would use a neural network with convolutional layers to extract features from the images, and then use fully connected layers to classify the images.

Neural Network Example

For more information on neural networks, check out our Deep Learning Specialization.

References