A curated list of awesome deep learning resources, inspired by awesome-computer-vision.
- An overview of gradient descent optimization algorithms, a PhD student.
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy.
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava, Geoffrey Hinton, et al.
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, facebook researchers.
- Practical Recommendations for Gradient-Based Training of Deep Architectures, Yoshua Bengio.
- LeNet-5:Gradient-based learning applied to document recognition,Lécun Y, et al.
- AlexNet:ImageNet classification with deep convolutional neural networks,Krizhevsky A, et al.
- VggNet-16:Very Deep Convolutional Networks for Large-Scale Image Recognition, Simonyan K, et al.
- ResNet:Deep Residual Learning for Image Recognition, He K, et al.
- 1×1 conv:Network In Network, Lin M, et al.
- InceptionNet:Computer Vision and Pattern Recognition, Szegedy C, et al.
- CS231n: Convolutional Neural Networks for Visual Recognition, Andrej Karpathy.
- Machine Learning and having it Deep and Structured,Hung-yi Lee.
- Coursera.org-Deep learning, Andrew Ng.
- Deep Learning, Ian Goodfellow et al.
- Overview of mini-batch gradient descent, Geoffrey Hinton et al.
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