xiaojianfei,github

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

cik-cusip-mapping

provide cik to cusip links using 13G and 13D filings

deap

Distributed Evolutionary Algorithms in Python

deep_learning_in_asset_pricing

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138

gplearn

Genetic Programming in Python, with a scikit-learn inspired API

hs300_stock_predict

该项目用于对沪深300股票的预测，包括股票下载，数据清洗，LSTM 模型的训练，测试，以及实时预测

ipca

Instrumented Principal Components Analysis

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

llama

Inference code for LLaMA models

lstm_stock

基于LSTM的股票数据分析，数据来源于Tushare

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

optuna

A hyperparameter optimization framework

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

pytorch-tutorials-examples-and-books

PyTorch tutorials, examples and some books I found 【不定期更新】整理的PyTorch 最新版教程、例子和书籍

pytorch_word2vec

Use pytorch to implement word2vec

sec-edgar

Download all companies periodic reports, filings and forms from EDGAR database.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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