Neural network (overview)

Artificial neural networks are a powerful type of model capable of processing many types of data. Initially inspired by the connections between biological neural networks, modern artificial neural networks only bear slight resemblances at a high level to their biological counterparts. Nonetheless, the analogy remains conceptually useful and is reflected in some of the terminology used. Individual 'neurons' in the network receive variably-weighted input from numerous other neurons in the more superficial layers. Activation of any single neuron depends on the cumulative input of these more superficial neurons. They, in turn, connect to many deeper neurons, again with variable weightings.

There are two broad types of neural networks:

• fully connected networks
  • simple kind of neural network where every neuron on one layer is connected to every neuron on the next layer
• recurrent neural networks
  • neural network where part or all of the output from its previous step is used as input for its current step. This is very useful for working with a series of connected information, for example, videos.

The usefulness of neural networks stems from the fact that they are universal function approximators, meaning that given the appropriate parameters, they can represent a wide variety of interesting and dissimilar functions.

Furthermore, they are differentiable mathematical functions, that is for a given set of parameters, inputs and labels, one can find the gradient of the parameters concerning defined loss functions, in effect helping determine how these parameters should be altered in order to improve predictions.

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Artificial intelligence

• artificial intelligence (AI)
• imaging data sets
• computer-aided diagnosis (CAD)
• natural language processing
• machine learning (overview)
  • machine learning processes
    • supervised learning
    • unsupervised learning
      • clustering
    • reinforcement learning
    • evolutionary algorithms
    • validation split
  • machine learning models
    • linear regression
    • logistic regression
    • decision tree
    • random forest
    • support vector machine
    • feature scaling
    • linear discriminant analysis
    • neural network (overview)
      • neural network architectures
        • ​fully-connected neural network
        • recurrent neural network
        • convolutional neural network
• visualizing and understanding neural networks
  • generative adversarial networks (GANs)
  • heat maps
  • class activation mapping(CAM)
• common data preparation/preprocessing steps
  • DICOM to bitmap conversion
  • dimensionality reduction 
  • scaling
  • centering
  • normalization
  • principal component analysis
  • training, testing and validation datasets
  • augmentation
  • loss function
    • mean squared error
    • cross entropy
  • optimization algorithms 
    • ADAM 
    • momentum (Nesterov)
    • stochastic gradient descent
    • mini-batch gradient descent
  • regularisation
    • linear and quadratic
    • batch normalization 
  • ensembling
    • boosting
    • bagging
  • rule-based expert systems
• glossary
  • activation function
  • anomaly detection
  • automation bias
  • backpropagation
  • batch size
  • computer vision
  • concept drift
  • cost function
  • confusion matrix
  • convolution
  • cross validation
  • curse of dimensionality
  • dice similarity coefficient
  • dimensionality reduction
  • epoch
  • explainable artificial intelligence/XAI
  • feature extraction
  • federated learning
  • gradient descent
  • ground truth
  • hyperparameters
  • image registration
  • imputation
  • iteration
  • jaccard index
  • linear algebra
  • noise reduction
  • normalization
  • R (Programming language)
  • Python (Programming language)
  • segmentation 
  • semi-supervised learning
  • synthetic and augmented data
  • overfitting
  • transfer learning