Boris Banushev

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.

anomaliesinoptions. In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.

predictions. Jupyter Notebook

options-gpt. Python

TradX. Jupyter Notebook

ReducingMLcostsWithAWS. Jupyter Notebook

TheEquityEmbeddings. Jupyter Notebook

AWSGlue-SageMakerDebugger. Demo of using AWS Glue and SageMaker Debugger

CAPM_Databricks.

mistralcookbook. Jupyter Notebook

MaskRCNN. Jupyter Notebook

snowflake. Python