diff --git a/README.md b/README.md index 302c042c..9defaa6c 100644 --- a/README.md +++ b/README.md @@ -35,37 +35,52 @@ See the project homepage [here](http://camdavidsonpilon.github.io/Probabilistic- The below chapters are rendered via the *nbviewer* at [nbviewer.jupyter.org/](http://nbviewer.jupyter.org/), and is read-only and rendered in real-time. -Interactive notebooks + examples can be downloaded by cloning! +Interactive notebooks + examples can be downloaded by cloning! + +Alternatively, if you want to both *view and edit* the notebooks online without installing anything, you can launch them in [*Deepnote*](https://beta.deepnote.org/). ### PyMC2 -* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it. +* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) + Why we do it. * [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb) + Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include: - Inferring human behaviour changes from text message rates * [**Chapter 2: A little more on PyMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb) + We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include: - Detecting the frequency of cheating students, while avoiding liars - Calculating probabilities of the Challenger space-shuttle disaster * [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb) + We discuss how MCMC operates and diagnostic tools. Examples include: - Bayesian clustering with mixture models * [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC2.ipynb) + We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include: - Exploring a Kaggle dataset and the pitfalls of naive analysis - How to sort Reddit comments from best to worst (not as easy as you think) * [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC2.ipynb) + The introduction of loss functions and their (awesome) use in Bayesian methods. Examples include: - Solving the *Price is Right*'s Showdown - Optimizing financial predictions - Winning solution to the Kaggle Dark World's competition * [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC2.ipynb) + Probably the most important chapter. We draw on expert opinions to answer questions. Examples include: - Multi-Armed Bandits and the Bayesian Bandit solution. - What is the relationship between data sample size and prior? @@ -75,33 +90,46 @@ Interactive notebooks + examples can be downloaded by cloning! ### PyMC3 -* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) Why we do it. +* [**Prologue:**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Prologue/Prologue.ipynb) + Why we do it. * [**Chapter 1: Introduction to Bayesian Methods**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb) + Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?" Examples include: - Inferring human behaviour changes from text message rates * [**Chapter 2: A little more on PyMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC3.ipynb) + We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include: - Detecting the frequency of cheating students, while avoiding liars - Calculating probabilities of the Challenger space-shuttle disaster * [**Chapter 3: Opening the Black Box of MCMC**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb) + We discuss how MCMC operates and diagnostic tools. Examples include: - Bayesian clustering with mixture models * [**Chapter 4: The Greatest Theorem Never Told**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb) + We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include: - Exploring a Kaggle dataset and the pitfalls of naive analysis - How to sort Reddit comments from best to worst (not as easy as you think) * [**Chapter 5: Would you rather lose an arm or a leg?**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter5_LossFunctions/Ch5_LossFunctions_PyMC3.ipynb) + The introduction of loss functions and their (awesome) use in Bayesian methods. Examples include: - Solving the *Price is Right*'s Showdown - Optimizing financial predictions - Winning solution to the Kaggle Dark World's competition * [**Chapter 6: Getting our *prior*-ities straight**](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb) + [](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter6_Priorities/Ch6_Priors_PyMC3.ipynb) + Probably the most important chapter. We draw on expert opinions to answer questions. Examples include: - Multi-Armed Bandits and the Bayesian Bandit solution. - What is the relationship between data sample size and prior? @@ -130,7 +158,9 @@ this book, though it comes with some dependencies. 2. The second, preferred, option is to use the nbviewer.jupyter.org site, which display Jupyter notebooks in the browser ([example](http://nbviewer.jupyter.org/urls/raw.github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter1_Introduction/Ch1_Introduction_PyMC2.ipynb)). The contents are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters. -3. PDFs are the least-preferred method to read the book, as PDFs are static and non-interactive. If PDFs are desired, they can be created dynamically using the [nbconvert](https://github.com/jupyter/nbconvert) utility. +3. The third option is to use [*Deepnote*](https://beta.deepnote.org), which allows you to view, edit and save the notebooks online in the browser ([example](https://beta.deepnote.org/launch?template=data-science&url=https%3A//github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb)). Just like with nbviewer.jupyter.org, the chapters are updated synchronously as commits are made to the book. You can use the Contents section above to link to the chapters. + +4. PDFs are the least-preferred method to read the book, as PDFs are static and non-interactive. If PDFs are desired, they can be created dynamically using the [nbconvert](https://github.com/jupyter/nbconvert) utility. Installation and configuration