This post explores the major architectures of deep neural networks, including RNN, LSTM, and CNN. It also compares the sequential and functional approaches in deep learning and discusses the types of feedforward neural networks. The post emphasizes the importance of choosing the right architecture for building neural networks and provides code examples for implementation.
•19m read time• From ai.gopubby.com
Table of contents
Neural network architecturesequential and Functional in Deep learningThe sequential modelThe functional modelFeedforward NetworkTypes of Feedforward Neural NetworksSingle Layer Feedforward Network (Perceptron)Multilayer Feedforward Network1. Recurrent neural network (RNN)Long Short-Term Memory — LSTM2.Fully recurrent network3. Elman and Jordan networks4. Convolutional Neural Networks (CNNs)PaddingZero PaddingValid PaddingPooling layersMax PoolingAverage PoolingFully connected layersSort: