lsy617004926 Goto Github PK

learnpythonforresearch

This repository provides everything you need to get started with Python for (social science) research.

lectures

Lecture notes for EC 607

lectures-1

Lecture material for Big Data in Economics (EC 410/510)

leetcode

Python & JAVA Solutions for Leetcode

leetcode-1

:pencil2: 算法相关知识储备 LeetCode with Python and JavaScript :books:

leetcode-2

🎓Leetcode solutions in Python 📚

leetcode-go

✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解

lightning

The most intuitive, flexible, way to build PyTorch models and lightning apps that glue together everything around the models, without the pain of infrastructure, cost management, scaling and everything else.

limperg_python

Repository with material for the Limperg Python course by Ties de Kok.

lob

Benchmark Dataset of Limit Order Book in China Markets

lstm

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

lstm-1

基于LSTM神经网络的时间序列预测

lsy

lsy617004926.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

machine-learning-course

:speech_balloon: Machine Learning Course with Python

machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.

macro_ml

Course Website on Macroeconomic Analysis with Machine Learning and Big Data

macroeconometrics

Course on Macroeconometrics (graduate level)

market-making-backtest

algo trading backtesting on BitMEX

mec_equil

'math+econ+code' masterclass on equilibrium transport and matching models in economics

mec_optim_2021-01

‘math+econ+code’ lectures on optimal transport and economic applications, January 2021

mercury-tutorial

Tutorial on the Mercury Computing Cluster

metrics-mathcamp-2021

mfe2019

Papers replicated in Quantiative Asset Management

micro-metrics

This is a 2nd Year PhD Course In Micro-econometrics

microprice

ml-and-data-science

Machine Learning and Data Mining: Regression [Linear (Selection and Shrinkage, Dimension Reduction, Beyond Linearity) & Non-Linear Regressions (Logistics, K-NN, Trees)], Cross Validation (LOOCV, K-Folds, Bias vs. Variance), Classification (LDA, QDA, K-NN, Logistic, Tree, SVM), Clustering (PCA, K-Means, Hierarchical) This course will provide an introduction to main topics in data mining / statistical learning, including: statistical foundations, data visualization, classification, regression, clustering. Emphasis will be on statistical learning methodology and the models, intuition, and assumptions behind it, as well as applications to real-world problems. You may find my final project in the stats 415 project folder. Project Summary  Implemented all the classifier learned throughout the semester to predict obesity rates in America as classified through the BMI with the best classifier as 7-fold KNN and prediction accuracy of 81.54%  Analyzed model selection methods to provide the most optimal model and finding the best predictors; concluded that BMI can be non-parametrically predicted based off income, eating habits, exercise habits and shopping habits

ml-finance_group

ml-quantamental

Machine Learning-Driven Quantamental Investing

mlfactor.github.io

Website dedicated to a book on machine learning for factor investing