List of Machine Learning Architectures

Certainly! Here is a comprehensive list of machine learning and deep learning architectures:

1. Feedforward Neural Networks (FNN) / Multilayer Perceptron (MLP)
2. Convolutional Neural Networks (CNN)
3. Recurrent Neural Networks (RNN)
— Long Short-Term Memory (LSTM)
— Gated Recurrent Unit (GRU)
— Bidirectional RNN
— Hierarchical RNN
— Attention-based RNN
4. Generative Adversarial Networks (GAN)
5. Autoencoders
— Variational Autoencoders (VAE)
— Sparse Autoencoders
— Denoising Autoencoders
6. Transformers
— Transformer Encoder
— Transformer Decoder
— BERT (Bidirectional Encoder Representations from Transformers)
— GPT (Generative Pre-trained Transformer)
— T5 (Text-To-Text Transfer Transformer)
7. Deep Reinforcement Learning
— Deep Q-Networks (DQN)
— Proximal Policy Optimization (PPO)
— Actor-Critic Networks
— Trust Region Policy Optimization (TRPO)
— Asynchronous Advantage Actor-Critic (A3C)
— Deep Deterministic Policy Gradient (DDPG)
8. Deep Boltzmann Machines (DBM)
9. Deep Belief Networks (DBN)
10. Restricted Boltzmann Machines (RBM)
11. Siamese Networks
12. Capsule Networks
13. Echo State Networks (ESN)
14. Extreme Learning Machines (ELM)
15. Radial Basis Function Networks (RBFN)
16. Deep Residual Networks (ResNet)
17. U-Net (used for image segmentation)
18. Mask R-CNN (used for object detection and instance segmentation)
19. Graph Neural Networks (GNN)
20. Adversarial Neural Networks (ANN)
21. NeuroEvolution of Augmenting Topologies (NEAT)
22. Temporal Convolutional Networks (TCN)
23. Spatial Transformer Networks (STN)
24. Continuous-Time Recurrent Neural Networks (CTRNN)
25. Echo State Gaussian Processes (ESGP)
26. PathNet
27. WaveNet (used for speech synthesis)
28. PointNet (used for point cloud classification)
29. DeepSets (used for set-to-set functions)
30. Neural Turing Machines (NTM)
31. Memory Networks
32. HyperNetworks
33. Particle Swarm Optimization (PSO)
34. Genetic Algorithms (GA)
35. Self-Organizing Maps (SOM)
36. Neural Architecture Search (NAS)

Please note that this list is not exhaustive, and new architectures continue to emerge as research progresses in the field of machine learning and deep learning.