Neural Network Model Architecture

A Neural Network Model Architecture is a model architecture for a neural network (that outlines the structure and design of the computational system).

• Context:
  • It can (typically) include an input layer that receives data, one or more hidden layers that process the data, and an output layer that produces the final prediction or classification.
  • It can (often) employ weights and activation functions to manage the flow and transformation of data through the network, allowing the model to learn from input data by adjusting weights based on learning algorithms.
  • It can (typically) be specialized into various forms depending on the task at hand, such as image recognition, natural language processing, or predictive modeling.
  • ...
• Example(s):
  • convolutional neural networks (CNNs), e.g. for image processing.
  • recurrent neural networks (RNNs), e.g. for sequential data
  • long short-term memory (LSTM) or gated recurrent unit (GRU), e.g. for natural language processing
  • self-organizing map (SOM), e.g. for clustering.
  • autoencoders (AE), e.g. for dimensionality reduction.
  • restricted Boltzmann machine (RBM), e.g. for generative modeling.
  • Transformer-based model architecture.
  • Decoder-only neural model architecture, e.g a GPT architecture.
  • ...
• Counter-Example(s):
  • Linear Model Architecture, which do not involve complex network structures or multiple layers of processing.
  • Support Vector Machines (SVM) Architecture, which rely on a different mathematical framework for classification and regression tasks.
  • Decision Tree Model Architecture and Random Forests, which use a hierarchical, tree-like model structure rather than interconnected neurons for decision making and classification.
• See: Deep Learning, Activation Function.

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References

2023

• chat
  • A Neural Network Model Architecture is a design or blueprint of an artificial neural network (ANN), which is a computational system inspired by biological neurons. A Neural Network Model Architecture consists of several components, such as:
    • Input layer: The data that we feed to the model is loaded into the input layer from external sources like a CSV file or a web service1.
    • Hidden layers: The hidden layers are what makes deep learning what it is today. They perform various computations and transformations on the input data and pass it to the next layer1. The number and type of hidden layers depend on the complexity and nature of the problem.
    • Output layer: The output layer takes input from preceding hidden layers and comes to a final prediction based on the model’s learnings1. The output can be a single value (regression), a class label (classification), or multiple values (multi-task learning).
    • Weights: The weights are numerical values that represent the strength of connections between nodes or neurons in different layers. They are updated during training using optimization algorithms like gradient descent2.
    • Activation functions: Activation functions control the amplitude of the output of each node or neuron. They introduce non-linearity into the model and allow it to learn complex patterns2. Some common activation functions are sigmoid, tanh, ReLU, softmax, etc.
    • There are many types of Neural Network Model Architectures that are used for different applications, such as convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequential data, long short-term memory (LSTM) or gated recurrent unit (GRU) for natural language processing, self-organizing map (SOM) for clustering, autoencoders (AE) for dimensionality reduction, restricted Boltzmann machine (RBM) for generative modeling, etc.23

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