Pinned Repositories
AmazonProductRecommendation-CF-ALS-Spark
We have chosen Amazon product sales data set comprising of sales activity and user ratings for each product. The idea is to create a product suggestion/recommendation system for each user based on his previous purchases and his rating for each one. A Collaborative Filtering model is built to predict the virtual ratings for the product that the user did not purchase. The system predicts the user rating for all the items and we display the products which user may be like, buy and rate higher.
autokeras
accessible AutoML for deep learning.
automated-feature-engineering
Automated feature engineering in Python with Featuretools
Bayes_Computing_Course
Bayesian-Analysis-with-Python
Bayesian Analysis with Python by Packt
Bayesian-Analysis-with-Python-Second-Edition
Bayesian Analysis with Python - Second Edition, published by Packt
Bayesian-HMM
A non-parametric Bayesian approach to Hidden Markov Models
Bayesian-Modelling-in-Python
A python tutorial on bayesian modeling techniques (PyMC3)
Bayesian-multivariate-time-series-causal-inference
R code for ''Bayesian method for causal inference in spatially-correlated multivariate time series''
pm-prophet
GAM timeseries modeling with auto-changepoint detection. Inspired by Facebook Prophet and implemented in PyMC3
AWalkInClouds's Repositories
AWalkInClouds/pm-prophet
GAM timeseries modeling with auto-changepoint detection. Inspired by Facebook Prophet and implemented in PyMC3
AWalkInClouds/autokeras
accessible AutoML for deep learning.
AWalkInClouds/Bayes_Computing_Course
AWalkInClouds/Bayesian-HMM
A non-parametric Bayesian approach to Hidden Markov Models
AWalkInClouds/Bayesian-multivariate-time-series-causal-inference
R code for ''Bayesian method for causal inference in spatially-correlated multivariate time series''
AWalkInClouds/Bios8366
Advanced Statistical Computing at Vanderbilt University Medical Center's Department of Biostatistics
AWalkInClouds/covid_bayesian_mcmc
Bayesian Markov Chain Monte Carlo Forecast for COVID-19
AWalkInClouds/Data-Analysis
Data Science Using Python
AWalkInClouds/gp_regression
A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)
AWalkInClouds/hctsa
Highly comparative time-series analysis code repository
AWalkInClouds/imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
AWalkInClouds/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
AWalkInClouds/lime
Lime: Explaining the predictions of any machine learning classifier
AWalkInClouds/machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
AWalkInClouds/machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
AWalkInClouds/mcmc_pydata_london_2019
PyData London 2019 Tutorial on Markov chain Monte Carlo with PyMC3
AWalkInClouds/mHMMbayes
R package: multilevel hidden Markov models using Bayesian estimation
AWalkInClouds/mlr3examples
General Discussion of mlr3 and examples
AWalkInClouds/mlxtend
A library of extension and helper modules for Python's data analysis and machine learning libraries.
AWalkInClouds/particles
Sequential Monte Carlo in python
AWalkInClouds/philentropy
Information Theory and Distance Quantification with R
AWalkInClouds/pyro
Deep universal probabilistic programming with Python and PyTorch
AWalkInClouds/PythonRobotics
Python sample codes for robotics algorithms.
AWalkInClouds/rstan
RStan, the R interface to Stan
AWalkInClouds/scipy2014_tutorial
Tutorial: Bayesian Statistical Analysis in Python
AWalkInClouds/SEED2.0
AWalkInClouds/shap
A unified approach to explain the output of any machine learning model.
AWalkInClouds/ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
AWalkInClouds/tsfresh
Automatic extraction of relevant features from time series:
AWalkInClouds/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow