Demonstration of MapR for Industrial IoT

Tensorflow for Time Series Applications

Code and notebooks for Econometric Theory

Converts a Shapefile, GeoJSON, or CSV to an equal area cartogram

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Курс по Python по работе с VK API

Python Productivity for ZYNQ

Python Tutorials

INFO 490: Advanced Data Science, offered in the Spring 2016 Semester at the University of Illinois

使用mxnet编写的kaggle CIFAR10比赛的代码

ETL with Python - Taught at DWH course 2017 (TAU)

Learning Heuristics for the TSP by Policy Gradient

Presentations for JuliaCon

Implementation and demonstration of backdrop in pytorch. Code and demonstration of GP dataset generator.

This Repository contains all tensorflow tutorials.

Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.

An implementation of NSGA-III in Python.