Layer Normalization (2016), J. Ba et al. Learning to learn by gradient descent by gradient descent (2016), M. Andrychowicz et al. Domain-adversarial training of neural networks (2016), Y. Ganin et al. WaveNet: A Generative Model for Raw Audio (2016)
Recent research on deep convolutional neural networks (CNNs) has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple CNN architectures that achieve that accuracy level. With equivalent a
High computational complexity hinders the widespread usage of Convolutional Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are arguably the most promising approach for reducing both execution time and power consumption.
Deep Learning Toolbox™提供了一个框架，用于设计和实现具有算法，预训练模型和应用程序的深度神经网络。您可以使用卷积神经网络（ConvNets，CNN）和长期短期记忆（LSTM）网络对图像，时间序列和文本数据进行分类和回归。应用程序和图表可帮助您可视化激活，编辑网络体系结构以及监控培训进度。 对于小型训练集，您可以使用预训练的深层网络模型（包括SqueezeNet，Inception-v3，ResNet-101，GoogLeNet和VGG-19）以及从TensorFlow™-
