### Probabilistic-Programming-and-Bayesian-Methods-for-Hackers - aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view

•        54

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion. After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of reading examples and trying to put the pieces together to understand the methods. There was simply not enough literature bridging theory to practice. The problem with my misunderstanding was the disconnect between Bayesian mathematics and probabilistic programming. That being said, I suffered then so the reader would not have to now. This book attempts to bridge the gap.

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

 Tags bayesian-methods pymc mathematical-analysis jupyter-notebook data-science statistics Implementation Jupyter Notebook License Public Platform

## edward - A probabilistic programming language in TensorFlow

•    Jupyter

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

## probability - Probabilistic reasoning and statistical analysis in TensorFlow

•    Jupyter

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation. Our probabilistic machine learning tools are structured as follows.

## PyMC3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

•    Python PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.

## Bayesian-Modelling-in-Python - A python tutorial on bayesian modeling techniques (PyMC3)

•    Jupyter

Welcome to "Bayesian Modelling in Python" - a tutorial for those interested in learning how to apply bayesian modelling techniques in python (PYMC3). This tutorial doesn't aim to be a bayesian statistics tutorial - but rather a programming cookbook for those who understand the fundamental of bayesian statistics and want to learn how to build bayesian models using python. The tutorial sections and topics can be seen below. Statistics is a topic that never resonated with me throughout university. The frequentist techniques that we were taught (p-values etc) felt contrived and ultimately I turned my back on statistics as a topic that I wasn't interested in.

## Data-Science-45min-Intros - Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

•    Jupyter

Every week*, our data science team @Gnip (aka @TwitterBoulder) gets together for about 50 minutes to learn something. While these started as opportunities to collectively "raise the tide" on common stumbling blocks in data munging and analysis tasks, they have since grown to machine learning, statistics, and general programming topics. Anything that will help us do our jobs better is fair game.

## spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

•    Jupyter

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

## Pyro - Deep universal probabilistic programming with Python and PyTorch

•    Python Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

## data-science-your-way - Ways of doing Data Science Engineering and Machine Learning in R and Python

•    Jupyter

These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

## python-for-data-analysis - An introduction to data science using Python and Pandas with Jupyter notebooks

•    Jupyter

Course in data science. Learn to analyze data of all types using the Python programming language. No programming experience is necessary. Note: O'Reilly Media titles are free to UCSD affiliates with Safari Books Online.

## statistical-analysis-python-tutorial - Statistical Data Analysis in Python

•    HTML

Chris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

## BDA_py_demos - Bayesian Data Analysis demos for Python

•    Jupyter

to interactively run the IPython Notebooks in the browser. This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).

## pandas-videos - Jupyter notebook and datasets from the pandas Q&A video series

•    Jupyter

Read about the series, and view all of the videos on one page: Easier data analysis in Python with pandas.

## hackermath - Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

•    Jupyter

Math literacy, including proficiency in Linear Algebra and Statistics,is a must for anyone pursuing a career in data science. The goal of this workshop is to introduce some key concepts from these domains that get used repeatedly in data science applications. Our approach is what we call the “Hacker’s way”. Instead of going back to formulae and proofs, we teach the concepts by writing code. And in practical applications. Concepts don’t remain sticky if the usage is never taught. The focus will be on depth rather than breadth. Three areas are chosen - Hypothesis Testing, Supervised Learning and Unsupervised Learning. They will be covered to sufficient depth - 50% of the time will be on the concepts and 50% of the time will be spent coding them.

## Data-Analysis-and-Machine-Learning-Projects - Repository of teaching materials, code, and data for my data analysis and machine learning projects

•    Jupyter

This is a repository of teaching materials, code, and data for my data analysis and machine learning projects.Each repository will (usually) correspond to one of the blog posts on my web site.

## Jupyter - Web-based notebook environment for interactive computing

•    Python The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more. It supports over 40 programming languages.

## pymc - PyMC: Bayesian Stochastic Modelling in Python (for PyMC3: https://github.com/pymc-devs/pymc3)

•    Fortran

NOTE: The development version of PyMC (version 3) has been moved to its own repository called pymc3.PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics.

## Python-for-Probability-Statistics-and-Machine-Learning - Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

•    Jupyter

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

## deepLearningBook-Notes - Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)

•    Jupyter

This content is part of a series following the chapter 2 on linear algebra from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts. I'd like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In my opinion, it is one of the bedrock of machine learning, deep learning and data science.

## data-science-with-ruby - Practical Data Science with Ruby based tools.

•    Ruby

Data Science is a new "sexy" buzzword without specific meaning but often used to substitute Statistics, Scientific Computing, Text and Data Mining and Visualization, Machine Learning, Data Processing and Warehousing as well as Retrieval Algorithms of any kind. This curated list comprises awesome tutorials, libraries, information sources about various Data Science applications using the Ruby programming language.

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