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adv_fin_ml_exercises

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

aiweek1

alphapy

Automated Machine Learning [AutoML] for Python

apriori

Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules

awesome-bigdata

A curated list of awesome big data frameworks, ressources and other awesomeness.

awesome-go

A curated list of awesome Go frameworks, libraries and software

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-python-cn

Python资源大全中文版，内容包括：Web框架、网络爬虫、网络内容提取、模板引擎、数据库、数据可视化、图片处理、文本处理、自然语言处理、机器学习、日志、代码分析等

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

awesome-ts-anomaly-detection

List of tools & datasets for anomaly detection on time-series data.

bi

www.steebi.com Datamining, Visualization, Machine-Learning, Big data

chainer-chemistry

Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry

classificaio

This repository contains ClassificaIO, a Python package that provides a graphical user interface (GUI) for machine learning algorithms from scikit-learn.

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

data_curation_workflow

deepfacelab

DeepFaceLab is the leading software for creating deepfakes.

dev-books

收集网络上的开发书籍

distributed

Distributed computation in Python

dltk

Deep Learning Toolkit for Medical Image Analysis

docker-stacks

Ready-to-run Docker images containing Jupyter applications

fastchat

The release repo for "Vicuna: An Open Chatbot Impressing GPT-4"

fay

Fay是一个完整的开源项目，包含Fay控制器及数字人模型，可灵活组合出不同的应用场景：虚拟主播、现场推销货、商品导购、语音助理、远程语音助理、数字人互动、数字人面试官及心理测评、贾维斯、Her。 开源项目，非产品试用！！！

finance_ml

Advances in Financial Machine Learning

financial-data-structures

This program is to help users create structured financial data in the form of time, tick, volume, and dollar bars from unstructured tick data. The user passes tick data to the create_bars(data, units=1000, type='tick') function and it returns the desired structured data. Everything can be found in the main.py file. I left lots of comments in the code. These bars are used throughout the text book (*Advances in Financial Machine Learning, By Marcos Lopex de Prado, 2018, pg 25*) to build the more interesting features for predicting financial time series data. A great paper to read more about how the tick, volume, and dollar bars have better statistical properties to standard time sampled data is: [The Volume Clock: Insights into the high frequency paradigm, Lopez de Prado, et al](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2034858) Note: Please make sure you unzip the ES_Trades.csv.zip found in raw_tick_data, it was too big to upload without zipping it first.

first-order-model

This repository contains the source code for the paper First Order Motion Model for Image Animation

generative_agents

Generative Agents: Interactive Simulacra of Human Behavior

golearn

Machine Learning for Go

gym

A toolkit for developing and comparing reinforcement learning algorithms.

hosts

无障碍上网-你~懂得！[更新频率7d，需要请 fork]