Linking pages
- How Attention works in Deep Learning: understanding the attention mechanism in sequence models | AI Summer https://theaisummer.com/attention/ 0 comments
- In-layer normalization techniques for training very deep neural networks | AI Summer https://theaisummer.com/normalization/ 0 comments
- How Transformers work in deep learning and NLP: an intuitive introduction | AI Summer https://theaisummer.com/transformer/ 0 comments
- Neural Architecture Search (NAS): basic principles and different approaches | AI Summer https://theaisummer.com/neural-architecture-search/ 0 comments
Linked pages
- Understanding LSTM Networks -- colah's blog https://colah.github.io/posts/2015-08-Understanding-LSTMs/ 64 comments
- A Recipe for Training Neural Networks http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines 39 comments
- Hadamard product (matrices) - Wikipedia https://en.wikipedia.org/wiki/Hadamard_product_(matrices) 9 comments
- Sigmoid function - Wikipedia https://en.wikipedia.org/wiki/Sigmoid_function 6 comments
- Andrej Karpathy (@karpathy) / Twitter https://twitter.com/karpathy 2 comments
- Understanding the receptive field of deep convolutional networks | AI Summer https://theaisummer.com/receptive-field/ 1 comment
- Tips for Training Recurrent Neural Networks http://danijar.com/tips-for-training-recurrent-neural-networks/ 0 comments
- Computing Receptive Fields of Convolutional Neural Networks https://distill.pub/2019/computing-receptive-fields 0 comments
- [1503.04069] LSTM: A Search Space Odyssey http://arxiv.org/abs/1503.04069 0 comments
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