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Neural Network Architecture Variants

Neural Network Architecture


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Neural Network Architecture Variants

Neural Network Architecture Variants refer to different structures and designs of neural networks used in machine learning and artificial intelligence. These variants include Feedforward Neural Networks like Perceptrons and Multi-Layer Perceptrons (MLP), as well as Deep Feedforward Networks. Other popular variants are Convolutional Neural Networks (CNNs) such as LeNet, AlexNet, VGGNet, ResNet, and InceptionNet, which are commonly used in image recognition tasks. Recurrent Neural Networks (RNNs) like Vanilla RNNs, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNNs are used for sequential data processing. Additionally, Autoencoders, Generative Adversarial Networks (GANs), and Transformer Networks like BERT, GPT, and T5 are also important variants in the field of neural network architecture.

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