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banksim

Agent-Based model of the Banking System

bayesian-analysis-with-python-second-edition

Bayesian Analysis with Python - Second Edition, published by Packt

bayesian-machine-learning

Notebooks about Bayesian methods for machine learning

blog

This repository is meant to store the code examples used in articles posted on www.insidelearningmachines.com. Each article on the website will have an associated jupyter notebook here with the same name.

deep-learning-paper-review-and-practice

꼼꼼한 딥러닝 논문 리뷰와 코드 실습

dxy

forecastinginflation

Forecasting Inflation in a data-rich environment: the benefits of machine learning methods

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

how-to-install-tensorflow-on-windows

machinelearningcrisisprediction

ml_projection_toolbox

Simple wrapper for machine learning models in the context of lead-lag projection modelling.

nowcasting

papers

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

pymc

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

python101

resources

PyMC3 educational resources

stat_rethinking_2022

Statistical Rethinking course winter 2022

stock-price-prediction-using-gan

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

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.