Neuraxio's repositories
Neuraxle
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
New-Empty-Python-Project-Base
The Perfect Python Project Template. Bored of coding anew the same thing for your new Python projects? Here is what you need. Click below on the "use this template" green button to start using it instantly. Rename the "project" folder and all references to this folder to customize your project name.
Kata-Clean-Machine-Learning-From-Dirty-Code
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.
Neuraxle-TensorFlow
TensorFlow steps, savers, and utilities for Neuraxle.
HAR-stacked-residual-bidir-LSTMs
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.
LSTM-Human-Activity-Recognition
Human activity recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) - Guillaume Chevalier
Multilingual-Latent-Dirichlet-Allocation-LDA
A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.
www.neuraxle.org
This is the HTML output code for the generated Neuraxle Documentation website, hosted by GitHub.
Awesome-Neuraxle
A curated list for resources on Neuraxle.
seq2seq-signal-prediction
Signal prediction with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
Autoencoder-TensorBoard-t-SNE
Plugging TensorBoard in an autoencoder on the MNIST dataset for demonstrating t-SNE embeddings visualization of unsupervised machine learning.
Awesome-Deep-Learning-Resources
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
filtering-stft-and-laplace-transform
Simple demo of filtering signal with an LP filter and plotting its STFT and Laplace transform, in Python.
GloVe-as-a-TensorFlow-Embedding-Layer
Taking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer INSIDE THE GPU. Therefore, you only need to send the INDEX of the words through the GPU data transfer bus, reducing data transfer overhead.
Hyperopt-Keras-CNN-CIFAR-100
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Linear-Attention-Recurrent-Neural-Network
A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN.
Predict-if-salary-is-over-50k-with-Keras
Predict whether income exceeds $50K/yr based on census data of the "Adult Dataset". Also known as "Census Income" dataset.
python-caffe-custom-cifar-100-conv-net
Custom convolutional neural network on cifar-100 dataset for image classification. Images and their labels are processed to HDF5 data format for use in Caffe.
python-conv-lib
A lightweight library to do for-loop-styled convolution passes on your iterable objects (e.g.: on a list).
PyTorch-Dynamic-RNN-Attention-Decoder-Tree
This is code I wrote within less than an hour so as to very roughly draft how I would code a Dynamic RNN Attention Decoder Tree with PyTorch.
scikit-learn-digit-recognition
Digit Recognition with scikit-learn's Bernoulli RBM and Logistic Classifier
Smoothly-Blend-Image-Patches
Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye.
staged-recipes
A place to submit conda recipes before they become fully fledged conda-forge feedstocks