100 nn models

It seems like you're asking for information or examples related to "100 neural network models." While there are many types of neural network architectures devised for various tasks, listing 100 distinct models or types might be overwhelming. Instead, I can provide you with a categorized list of popular neural network architectures often used in different domains: ### 1. **Feedforward Neural Networks (FNN)** - Multi-Layer Perceptron (MLP) - Radial Basis Function (RBF) Network ### 2. **Convolutional Neural Networks (CNN)** - LeNet - AlexNet - VGGNet - GoogLeNet (Inception) - ResNet - DenseNet - MobileNet - EfficientNet - SqueezeNet - U-Net (for segmentation) ### 3. **Recurrent Neural Networks (RNN)** - Vanilla RNN - Long Short-Term Memory (LSTM) - Gated Recurrent Unit (GRU) ### 4. **Transformer Models** - Transformer (original) - BERT (Bidirectional Encoder Representations from Transformers) - GPT (Generative Pre-trained Transformer) - T5 (Text-to-Text Transfer Transformer) - RoBERTa (A Robustly Optimized BERT Pretraining Approach) - ViT (Vision Transformer) ### 5. **Generative Models** - Generative Adversarial Networks (GANs) - DCGAN (Deep Convolutional GAN) - CycleGAN - StyleGAN - Variational Autoencoders (VAEs) ### 6. **Self-Organizing Maps (SOM)** - Kohonen Network ### 7. **Graph Neural Networks (GNN)** - Graph Convolutional Networks (GCN) - Graph Attention Networks (GAT) ### 8. **Autoencoders** - Denoising Autoencoder - Sparse Autoencoder - Variational Autoencoder (VAE) ### 9. **Capsule Networks** - CapsNet ### 10. **Hybrid Models** - CNN-RNN for video analysis - Attention-based models for sequence tasks ### 11. **Specialized Networks** - Siamese Networks (for similarity learning) - Neural Turing Machines - Attention Mechanisms in neural networks ### 12. **Other Notable Models** - Fast R-CNN (for object detection) - YOLO (You Only Look Once) - SSD (Single Shot MultiBox Detector) ### 13. **Ensemble Methods** - Model ensembles using various architectures ### Considerations: - Each model can be modified or configured in numerous ways, which can lead to many variations depending on the application (e.g., image classification, natural language processing, reinforcement learning). - Within these architectures, there are various techniques and layers that can be used, such as dropout layers, pooling layers, and normalization techniques. If you have a specific focus or need more details about certain types of models or their applications, feel free to ask!