SGNN-Self-Governing-Neural-Networks-Projection-Layer in guillaume-chevalier/Awesome-Deep-Learning-Resources
Attempt at reproducing a SGNN's projection layer, but with word n-grams instead of skip-grams. Paper and more: http://aclweb.org/anthology/D18-1105
created at Dec. 20, 2018, 10:08 p.m.
How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks in markusschanta/awesome-jupyter
Growing the code out of your notebooks - the right way.
created at Oct. 13, 2018, 9:13 a.m.
Smoothly-Blend-Image-Patches in guillaume-chevalier/Awesome-Deep-Learning-Resources
Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye.
created at Aug. 25, 2017, 8:33 p.m.
Hyperopt-Keras-CNN-CIFAR-100 in guillaume-chevalier/Awesome-Deep-Learning-Resources
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
created at May 27, 2017, 6:15 p.m.
seq2seq-signal-prediction in jtoy/awesome-tensorflow, ujjwalkarn/Machine-Learning-Tutorials, guillaume-chevalier/Awesome-Deep-Learning-Resources
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
created at March 31, 2017, 7:26 p.m.
filtering-stft-and-laplace-transform in guillaume-chevalier/Awesome-Deep-Learning-Resources
Simple demo of filtering signal with an LPF and plotting its Short-Time Fourier Transform (STFT) and Laplace transform, in Python.
created at Jan. 22, 2017, 6:03 a.m.
Awesome-Deep-Learning-Resources (13) in sindresorhus/awesome
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
created at Nov. 27, 2016, 5:41 p.m.
Unknown languages
HAR-stacked-residual-bidir-LSTMs in guillaume-chevalier/Awesome-Deep-Learning-Resources
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
created at Nov. 26, 2016, 2:41 p.m.
LSTM-Human-Activity-Recognition in jtoy/awesome-tensorflow, guillaume-chevalier/Awesome-Deep-Learning-Resources
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
created at May 18, 2016, 2 a.m.