Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
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Updated
Oct 31, 2020 - Python
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
Educational deep learning library in plain Numpy.
🔬 Nano size Theano LSTM module
Artificial Intelligence Learning Notes.
Complementary code for the Targeted Dropout paper
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
repo that holds code for improving on dropout using Stochastic Delta Rule
Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.
[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
Implementation of DropBlock in Pytorch
Bayesian Neural Network in PyTorch
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
Implementation of key concepts of neuralnetwork via numpy
Win probability predictions for League of Legends matches using neural networks
[ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal, Shiwei Liu, Zhangyang Wang
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