Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
-
Updated
Oct 31, 2020 - Python
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
🔬 Nano size Theano LSTM module
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
Educational deep learning library in plain Numpy.
Artificial Intelligence Learning Notes.
Complementary code for the Targeted Dropout paper
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.
Bayesian Neural Network in PyTorch
[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
Short description for quick search
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
repo that holds code for improving on dropout using Stochastic Delta Rule
DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020
Implementation of key concepts of neuralnetwork via numpy
Add a description, image, and links to the dropout topic page so that developers can more easily learn about it.
To associate your repository with the dropout topic, visit your repo's landing page and select "manage topics."