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lstm

Minimal, clean example of lstm neural network training in python, for learning purposes.

lstm-neural-network-for-time-series-prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

lstm-stock-prediction

Day-Trading algorithm using LSTM to predict intraday stock movement

lstm-text-classification

用长短期记忆网络LSTM来处理文本分类

lsuvinit

Reference caffe implementation of LSUV initialization

luca

Bookkeeping in Python

lucas-kanade-object-tracking

Lucas-Kanade Object Tracking and Background Subtraction in Videos

luci-app-shadowsocks

OpenWrt/LEDE LuCI for Shadowsocks-libev

lucid

A collection of infrastructure and tools for research in neural network interpretability.

luckymoney

微信红包，反编译微信客户端，进行代码插桩，造福无root或无xposed用户

luigi

Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

lumen

Illuminate your data.

luxcore

LuxCore source repository

lvis-api

Python API for LVIS Dataset

lvvd

Listed Volatility and Variance Derivatives (Wiley Finance)

lxml

The lxml XML toolkit for Python

lyft-3d-challenge

lyman

A platform for analyzing neuroimaging data in Python

lynx-toolbox

Lynx Matlab Toolbox

lynxkite

The complete graph data science platform

lyra

A simple to use, composable, command line parser for C++ 11 and beyond

lyra-1

An interactive, graphical Visualization Design Environment (VDE)

lyrical-sentiment-analysis

Using Spotify API to obtain audio feature data and lyrics and Tableau to produce sick visualizations.

m-unit-tests

This project has moved to https://git.dynare.org/Dynare/m-unit-tests.git

m4econ

Matlab Code Examples

m5-methods

Data, Benchmarks, and methods submitted to the M5 forecasting competition

ma322_scientific-computing

ma374_financial_engg_lab

maassim

Agent-based simulator for two sided urban mobility markets

mac-network

Implementation for the paper "Compositional Attention Networks for Machine Reasoning" (Hudson and Manning, ICLR 2018)