Attention of DL: Based on the ClutteredMNIST handwritten digital picture data set, use CNN_Init and ST_CNN algorithms (CNN+SpatialTransformer) to achieve multi-class prediction - Code World

Attention of DL: Based on the ClutteredMNIST handwritten digital picture data set, use CNN_Init and ST_CNN algorithms (CNN+SpatialTransformer) to achieve multi-class prediction

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Origin blog.csdn.net/qq_41185868/article/details/114159918

Attention of DL: Based on the ClutteredMNIST handwritten digital picture data set, use CNN_Init and ST_CNN algorithms (CNN+SpatialTransformer) to achieve multi-class prediction

Внимание DL: на основе набора рукописных цифровых изображений ClutteredMNIST используйте алгоритмы CNN_Init и ST_CNN (CNN + SpatialTransformer) для достижения мультиклассового прогнозирования.

Implementation of RNN and CNN based on Pytorch's MNIST handwritten data set

Classification prediction | Matlab implements data classification prediction based on TSOA-CNN-GRU-Attention

Multivariate classification prediction | Matlab based on TSOA-CNN-GRU-Attention Transit optimized convolutional neural network combined with gated recurrent unit fusion attention mechanism for data classification prediction

Python: Using CNN neural network to achieve CSV data classification prediction

KNN CNN data set preparation for handwritten digit recognition

Classification prediction | MATLAB implements SCNGO-CNN-LSTM-Attention data classification prediction

Classification prediction | MATLAB implements WOA-CNN-BiGRU-Attention data classification prediction

Classification prediction | MATLAB implements WOA-CNN-LSTM-Attention data classification prediction

keras achieve mnist handwritten digital data set of training

Construction and application of CNN based on MNIST data set

Regression prediction | MATLAB implements CNN-BiGRU-Attention multi-input single-output regression prediction

MNIST handwritten digital identification data set (based tensorflow)

Bearing life prediction practice based on deep learning, development of CNN, fusion of LSTM/GRU/ATTENTION

Kaggle- handwritten numbers (based on Pytorch and CNN)

Multiple regression prediction | Matlab regression prediction based on convolutional neural network-long short-term memory network combined with attention mechanism (CNN-LSTM-Attention), multivariate input model

Multiple regression prediction | Matlab based on convolutional neural network-bidirectional gated recurrent unit combined with attention mechanism (CNN-BIGRU-Attention) regression prediction, multivariate input model

Multiple regression prediction | Matlab based on convolutional neural network-gated recurrent unit combined with attention mechanism (CNN-GRU-Attention) regression prediction, multivariate input model

Multi-class prediction based on adaptive genetic algorithm to optimize BP neural network, multi-class prediction based on GA-BP

Multidimensional time series | MATLAB implements WOA-CNN-GRU-Attention multi-variable time series prediction (SE attention mechanism)

Multidimensional time series | MATLAB implements WOA-CNN-BiLSTM-Attention multi-variable time series prediction (SE attention mechanism)

PyTorch Basics-Using Convolutional Neural Network CNN to Realize Handwritten Data Set Recognition-07

CNN+LSTM+Attention realizes time series prediction

Time series prediction: CNN+LSTM+Attention model in practice

Keras achieve CNN: Handwritten digit recognition accuracy rate of 99.6%

Multivariate Classification Prediction | Matlab Data Classification Prediction Based on RIME-CNN Frost and Ice Optimization Convolutional Neural Network

Multivariate classification prediction | Matlab optimizes convolutional neural network data classification prediction based on EVO-CNN energy valley

KNN CNN recognition handwritten digital core code understanding

CNN Convolutional Neural Network Handwritten Digital Image Recognition (Deep Learning)

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