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AI Basics

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Machine Learning Frameworks

Types of Neural Networks

  • Feed Forward Neural Network (FfNN)
    • Perceptron
    • Multi Layer Perceptron (MLP)
    • Autoencoders
    • Radial Basis Function Neural Networks (RBF)
  • Convolutional Neural Network (CNN)
  • Recurrent Neural Network (RNN)
    • Long Short Term Memory (LSTM)
    • Gated Recurrent Unit (GRU)
    • Attention based
  • Generative Adversarial Network (GAN)
  • Transformers
  • Sequence to Sequence Neural Networks (consists of two RNNs)
  • Modular Neural Networks
More I got from OpenAssistant's AI (have to verify):
  1. Feed Forward Neural Network (FfNN)
  2. Feedforward neural networks with Rectified Linear Units (ReLU) activation
  3. Reservoir Computing (RC)
  4. RNNs
  5. Echo state networks (ESN)
  6. Autoencoders
  7. Variational Autoencoders (VAE)
  8. Denoising Autoencoders (DAE)
  9. Dual Avatar Autoencoders (DAAE)
  10. Denoising Adversarial Networks (DAN)
  11. Energy-Based Architectures
  12. Variational Bayesian Inference

Terms

  • Frameworks
  • Networks - Models and Networks are often used interchangeably. But mainly the difference is that algorithms are implemented on networks to create models.
  • AI Algorithms
  • Models - Products of AI Training, by running a machine learning algorithm on training data
  • Inference - Inference is where deep learning capabilities learned during training are put to work

Backlinks

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