ardakzhanali Goto Github PK

corehackathon-1

BI-хакатон на маркетинговых данных под Power BI

data

Данные для участников хакатона

data-engineering

Getting Started with Data Enngineering

deepai

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

finquant

A program for financial portfolio management, analysis and optimisation.

markov-chain

R based marketing attribution and basic budget optimization using Markov Chains

pro-power-bi-architecture

Source code for 'Pro Power BI Architecture' by Reza Rad

quantitative-notebooks

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

r

robo-chef

Final project for MIT 6.864, along with Nico Rakover and Ambika Krishnamachar

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.