This lesson is being piloted (Beta version)
If you teach this lesson, please tell the authors and provide feedback by opening an issue in the source repository

Introduction to artificial neural networks in Python: Glossary

Key Points

  • Algorithms can be used to detect disease in chest X-rays.

  • In NumPy, RGB images are usually stored as 3-dimensional arrays.

Data preparation
  • Data augmentation can help to avoid overfitting.

Neural networks
  • Dense layers, also known as fully connected layers, are an important building block in most neural network architectures. In a dense layer, each neuron is connected to every neuron in the preceeding layer.

  • Dropout is a method that helps to prevent overfitting by temporarily removing neurons from the network.

  • The Rectified Linear Unit (ReLU) is an activation function that outputs an input if it is positive, and outputs zero if it is not.

  • Convolutional neural networks are typically used for imaging tasks.

Training and evaluation
  • During the training process we iteratively update the model to minimise error.

  • Saliency maps are a popular form of explainability for imaging models.

  • Saliency maps should be used cautiously.