Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

A repo for new open source contributors to begin with open source contribution. Contribute and earn awesome swags.

Oxford Deep NLP 2017 course - Practical 1: word2vec

Notes and Summaries on ML-related Research Papers (with optional implementations)

Visualising LIDAR data from KITTI dataset.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

A day to day plan for this challenge (50 Days of Machine Learning) . Covers both theoretical and practical aspects

Bonus materials, exercises, and example projects for our Python tutorials

A Pytorch Computer Vision template to quick start your next project! 🚀🚀

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

Materials for GWU DNSC 6279 and DNSC 6290.

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

Visualisation tool for CNNs in pytorch

CardIO is a library for data science research of heart signals

Unsupervised clustering with (Gaussian mixture) VAEs

Coding the Machine Learning Tutorial for Learning to Learn

edaviz - Python library for Exploratory Data Analysis and Visualization in Jupyter Notebook or Jupyter Lab

Tools for using a variational auto-encoder for latent image encoding and generation.

Implementation of Factorization Machines on Spark using parallel stochastic gradient descent (python and scala)

PyTorch Implementation of Deformable Convolution