hominine720202 Goto Github PK

10k-mda-section

Extract the Management Discussion and Analyses (MD&A) section from 10K Financial Statements

10k-nlp-stock-prediction

Prediction of future stock returns through natural language processing of company financial statements.

analyzing-stock-sentiment-from-tweets

Analyzing Stock Sentiment from Tweets using NLP and Deep Learning

awesome-machine-learning

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

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

bert

TensorFlow code and pre-trained models for BERT

bert-for-tf2

A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.

bert-ner-scienceie

BERT-based Scientific Keyphrase Extraction

bert_ie

breakout-strategy

camelot

Camelot: PDF Table Extraction for Humans

colab-cli

✨Experience better workflow with google colab, local jupyter notebooks and git

combine-signals-for-enhanced-alpha

Combine Signals for Enhanced Alpha combines multiple signals and enhanced alpha for trading using machine learning

craw_national_data

抓取国家统计局数据

daguan_2019_rank9

达观信息提取比赛第九名代码

deep-learning

deepwalk

DeepWalk - Deep Learning for Graphs

deepwalk_keras_igraph

Reimplementation of deepwalk algorithm from https://github.com/phanein/deepwalk

deterministic-variational-inference

Sample code for running deterministic variational inference to train Bayesian neural networks

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

doubly-stochastic-deep-gaussian-process

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.

doubly-stochastic-dgp

Deep Gaussian Processes with Doubly Stochastic Variational Inference

dpdqn-continuous-control-in-deep-reinforcement-learning-with-direct-policy-derivation-from-q-network

financial-statements-text-analysis

Documentation and code for downloading, cleaning, munging, and analyzing financial statements filed by publicly traded companies with the SEC

financial_statements

financial_statements-1

A Visual Comparison of Financial Statements with Python

financialstatement_ml

financialstatementcapture

深信服数据平台，python接口

finbert

A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097