#deep-learning
13 events tagged with this keyword
Backpropagation Algorithm Revival
Rumelbart, Hinton, and Williams popularize the backpropagation algorithm, reviving neural network research.
LeCun CNN for Handwriting Recognition
Yann LeCun at Bell Labs develops convolutional neural networks, successfully applying them to handwritten digit recognition.
LSTM Neural Network
Hochreiter and Schmidhuber propose LSTM, solving the long-term dependency problem of recurrent neural networks.
LeNet-5 and MNIST
Yann LeCun publishes LeNet-5 architecture and MNIST dataset, becoming classic benchmarks for deep learning.
Deep Belief Network: Deep Learning Revival
Geoffrey Hinton proposes Deep Belief Networks, pioneering effective methods for training deep learning.
AlexNet: Deep Learning Breakthrough
AlexNet wins the ImageNet competition by a large margin, marking the beginning of the deep learning era.
GAN: Generative Adversarial Networks
Ian Goodfellow proposes Generative Adversarial Networks, pioneering a new paradigm for deep generative models.
Dropout Regularization
Geoffrey Hinton and colleagues propose Dropout technique, effectively preventing neural network overfitting.
VGGNet: Very Deep Convolutional Networks
Oxford VGG team proposes VGGNet, demonstrating that stacking small convolution kernels can build deeper networks.
ResNet: Residual Networks Breakthrough
Microsoft Research proposes ResNet, solving deep network training difficulties through residual connections.
TensorFlow Open Sourced
Google open sources TensorFlow deep learning framework, becoming one of the most popular machine learning platforms.
Microsoft Speech Recognition Reaches Human Parity
Microsoft announces its speech recognition system achieves human parity on Switchboard benchmark.
PyTorch Released
Facebook releases PyTorch deep learning framework, winning researchers over with its dynamic computation graph.