datasciences Goto Github PK

ad-optimization-reinforcement-learning

Using UCB and Thompson Sampling to optimize ads click performance

ad-papers

Papers on Computational Advertising

adanalysis

Optimizing ads is one of the most intellectually challenging jobs a data scientist can do. It is a really complex problem given the huge (really really huge) size of the datasets as well as number of features that can be used. Moreover, companies often spend huge amounts of money in ads and a small ad optimization improvement can be worth millions of dollars for the company. The goal of this project is to look at a few ad campaigns and analyze their current performance as well as predict their future performance.

ads-analysis

Recommendation system: Identify top 5 ad campaigns out of 40 in the dataset. Provide recommendations to improve the performance

advance-bayesian-modelling-with-pymc3

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

amss

Learn Bayesian Regression on simulated dataset

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-r

A curated list of awesome R packages, frameworks and software.

bayes-python

Bayesian Hierarchical Modeling from scratch in Python

bayes-r-jags-intro

An introduction to hierarchical Bayesian modelling with R, JAGS and STAN

bayes_computing_course

bayes_tutorial_2019

Introductory overview of Bayesian inference

bayesian-analysis-with-python

Bayesian Analysis with Python by Packt

bayesian-machine-learning

Notebooks related to Bayesian methods for machine learning

bayesian-modelling-in-python

A python tutorial on bayesian modeling techniques (PyMC3)

bayesian-multilevel-modeling-in-pymc3

bayesian-multilevel-modelling

bayesian-personalized-ranking

One-class recommendation algorithm from implicit feedback

bayesian_hierarchical_model

Hierarchical model to estimate average revenues by tee times

bayesian_inference-graphical_models

Bayesian machine learning, Bayesian deep learning, Probabilistic graphical models

bigdataclass

Two-day workshop that covers how to use R to interact databases and Spark

bios8366

Advanced Statistical Computing at Vanderbilt University Medical Center's Department of Biostatistics

brms

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

brmstools

Helper functions for brmsfit objects (DEPRECATED)

colab-notebooks

Colab notebooks exploring different Machine Learning topics.

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

deep-learning-with-r-notebooks

R notebooks for the code samples of the book "Deep Learning with R"

deeplearningclinicaldiagnostics

Here I show how to utilize Bayesian Deep Learning using PyMC3 for making more accurate and safer predictions for biomedical applications