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dropout
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Tensorflow tutorial from basic to hard
machine-learning
tutorial
neural-network
tensorflow
regression
cnn
dropout
generative-adversarial-network
gan
dqn
classification
rnn
autoencoder
tensorflow-tutorials
deep-q-network
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Feb 18, 2019 - Python
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
machine-learning
computer-vision
pytorch
dropout
regularization
convolutional-neural-networks
pytorch-implementation
dropblock
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Feb 22, 2019 - Python
python
machine-learning
tutorial
theano
neural-network
automatic-differentiation
recurrent-networks
lstm
gru
adadelta
dropout
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Nov 16, 2016 - Python
Implementations of CNNs, RNNs and deep learning techniques in pure Numpy
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May 16, 2020 - Python
Complementary code for the Targeted Dropout paper
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Sep 26, 2019 - Python
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
tensorflow
cnn
dropout
mnist
batch-normalization
mnist-classification
data-augmentation
ensemble-prediction
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Jul 26, 2018 - Python
repo that holds code for improving on dropout using Stochastic Delta Rule
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Feb 10, 2019 - Python
Artificial Intelligence Learning Notes.
machine-learning
reinforcement-learning
computer-vision
deep-learning
tensorflow
image-processing
cnn
dropout
vgg
lenet
alexnet
harris
sift
image-segmentation
image-augmentation
hog
image-denoising
dpm
image-enhancement
activation-function
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Jan 28, 2020 - Python
AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.
machine-learning
reinforcement-learning
deep-learning
recurrent-neural-networks
lstm
dropout
mnist
neural-turing-machines
question-answering
cartpole
lstm-model
lenet
convolutional-networks
convolutional-neural-networks
deep-q-network
computational-graphs
auto-differentiation
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Feb 27, 2020 - Python
Implementation of DropBlock in Pytorch
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Nov 4, 2018 - Python
PyTorch Implementations of Dropout Variants
pytorch
dropout
variational-inference
bayesian-neural-networks
local-reparametrization-trick
gaussian-dropout
variational-dropout
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Jan 7, 2018 - Jupyter Notebook
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
javascript
css
python
html
http
flask
computer-vision
tensorflow
convnet
image-processing
cnn
web-application
dropout
image-classification
convolutional-neural-network
fully-connected-network
max-pooling
relu
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May 2, 2018 - Python
The tools and syntax you need to code neural networks from day one.
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Sep 25, 2017 - Jupyter Notebook
My workshop on machine learning using python language to implement different algorithms
python
machine-learning
workshop
deep-learning
numpy
scikit-learn
keras
dropout
batch-normalization
neural-networks
matplotlib
convolutional-neural-networks
gradient-descent
backpropagation
optimization-algorithms
softmax
machine-learning-workshop
linear-classification
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Jan 24, 2020 - Jupyter Notebook
Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq and CITE-seq).
clustering
dropout
batch-normalization
imputation
cca
scrna-seq
diffusion-maps
clustering-algorithm
spike-inference
3d
umap
normalization
10xgenomics
cell-type-classification
intractive-graph
cite-seq
singel-cell-sequencing
pseudotime
scvdj-seq
icellr
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May 20, 2020 - R
Implementation of key concepts of neuralnetwork via numpy
neural-network
numpy
cnn
dropout
mnist
sgd
regularization
deeplearning
xavier-initializer
relu
cross-entropy-loss
numpy-neuralnet-exercise
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Feb 6, 2018 - Python
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
machine-learning
computer-vision
cnn
dropout
mnist
svhn
convolutional-neural-networks
confusion-matrix
google-street
convolutional-neural-network
tensorboard-visualizations
tesnorflow
svhn-classifier
grayscale-images
house-numbers
adam-optimizer
mini-batch-gradient-descent
one-hot-encode
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Mar 4, 2018 - Jupyter Notebook
Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
python
machine-learning
neural-network
paper
pytorch
dropout
bayesian-inference
posterior-probability
local-reparametrization-trick
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Nov 3, 2017 - Python
Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
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Jan 31, 2020 - Python
Bayesian Neural Network in PyTorch
neural-network
pytorch
artificial-intelligence
dropout
bayesian-inference
bayesian-neural-networks
bnn
concrete-dropout
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Sep 14, 2019 - Python
Imputation method for scRNA-seq based on low-rank approximation
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Aug 10, 2019 - R
Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Bayesian-Active-Learning/))
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Mar 30, 2020 - Jupyter Notebook
Understanding nuts and bolts of neural networks with PyTorch
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Sep 19, 2019
xXLaokoonXx
commented
Mar 27, 2018
Hi minihat,
I really love your idea and would like to do a similar Prediction.
While looking through the files in this repository it was pretty hard for me to understand the meaning of each file.
And just by your final report:
did you used Gold earned and Gold spent in the neuronal network?
if so you might have designed a great detector for the team with more unspent gold or more gold in tota
TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)
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Jun 7, 2017 - Python
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I tried some RNN regression learning based on the code in the "PyTorch-Tutorial/tutorial-contents/403_RNN_regressor.py" file, which did not work for me at all.
According to an accepted answer on stack-overflow (https://stackoverflow.com/questions/52857213/recurrent-network-rnn-wont-learn-a-very-simple-function-plots-shown-in-the-q?noredirect=1#comment92916825_52857213), it turns out that the li