Pinned Repositories
anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
bert
TensorFlow code and pre-trained models for BERT
code4goal-resume-parser
Solution for Code4Goal challenge
dash-oil-and-gas-demo
Dash Demo App - New York Oil and Gas
dptoolkit
The Data Productivity Toolkit is a collection of linux command-line tools designed to facilitate the analysis of text-based data sets.
Kaggle_CrowdFlower
1st Place Solution for Search Results Relevance Competition on Kaggle (https://www.kaggle.com/c/crowdflower-search-relevance)
LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
R-zoo
Clone of 'zoo' time-series R package from RForge
GTaneja's Repositories
GTaneja/R-zoo
Clone of 'zoo' time-series R package from RForge
GTaneja/anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
GTaneja/bert
TensorFlow code and pre-trained models for BERT
GTaneja/code4goal-resume-parser
Solution for Code4Goal challenge
GTaneja/dash-oil-and-gas-demo
Dash Demo App - New York Oil and Gas
GTaneja/dptoolkit
The Data Productivity Toolkit is a collection of linux command-line tools designed to facilitate the analysis of text-based data sets.
GTaneja/Kaggle_CrowdFlower
1st Place Solution for Search Results Relevance Competition on Kaggle (https://www.kaggle.com/c/crowdflower-search-relevance)
GTaneja/LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
GTaneja/mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
GTaneja/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
GTaneja/python-books
My Python Book Collection
GTaneja/python-fire
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
GTaneja/pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch
GTaneja/resp
GTaneja/Resume-Parser
Analyze, score and rank a collection of PDF resumes using machine learning
GTaneja/ResumeParser
Resume Parser using rule and machine-learning based approach. Developed using framework provided by GATE
GTaneja/ResumeParser-1
A script to parse PDF resumes, extract contact information, and check for required terms
GTaneja/stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
GTaneja/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.
GTaneja/tsfresh
Automatic extraction of relevant features from time series:
GTaneja/utilities
GTaneja/xgboost
Large-scale and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, on single node, hadoop yarn and more.