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Using UCB and Thompson Sampling to optimize ads click performance
Papers on Computational Advertising
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.
Recommendation system: Identify top 5 ad campaigns out of 40 in the dataset. Provide recommendations to improve the performance
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
Learn Bayesian Regression on simulated dataset
A curated list of awesome Deep Learning tutorials, projects and communities.
A curated list of awesome Machine Learning frameworks, libraries and software.
A curated list of awesome R packages, frameworks and software.
Bayesian Hierarchical Modeling from scratch in Python
An introduction to hierarchical Bayesian modelling with R, JAGS and STAN
Introductory overview of Bayesian inference
Bayesian Analysis with Python by Packt
Notebooks related to Bayesian methods for machine learning
A python tutorial on bayesian modeling techniques (PyMC3)
One-class recommendation algorithm from implicit feedback
Hierarchical model to estimate average revenues by tee times
Bayesian machine learning, Bayesian deep learning, Probabilistic graphical models
Two-day workshop that covers how to use R to interact databases and Spark
Advanced Statistical Computing at Vanderbilt University Medical Center's Department of Biostatistics
brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
Helper functions for brmsfit objects (DEPRECATED)
Colab notebooks exploring different Machine Learning topics.
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.
R notebooks for the code samples of the book "Deep Learning with R"
Here I show how to utilize Bayesian Deep Learning using PyMC3 for making more accurate and safer predictions for biomedical applications
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.