Applications of Deep Neural Networks with Keras
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the f
A Convergence Theory for Deep Learning via Over-Parameterization
Deep neural networks (DNNs) have demonstrated dominating performance in many fields; since AlexNet, networks used in practice are going wider and deeper. On the theoretical side, a long line of works has been focusing on training neural networks with one hidden layer. The theory of multi-layer networks remains largely unsettled. In this work, we prove why stochastic gradient descent (SGD) can find
An Analysis of Deep Neural Network Models for Practical Applications
Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of importan
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Pre-Trained Models: Past, Present and Future
Daisuke Okanoharaさんのツイート: "多層NNの最適化が難しそうなのになぜ成功するかについて、reluを使ったNNに対しデータが退化してなく、NNが過剰に多くのパラメータを備えていればSGDを使った学習は最適解に多項式時間で収束することが証明された。NNの未解決問題の一つが（おそらく）解かれた https://t.co/emWfXo1QO7"
Backdrop: Stochastic Backpropagation
