From ff854032dac92e5e4b4ebf4f93ed9c8f7e7ca026 Mon Sep 17 00:00:00 2001 From: Ejar Date: Thu, 6 Jan 2022 14:06:02 +0100 Subject: [PATCH] Fix typo --- Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb b/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb index 32366073..093086a9 100644 --- a/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb +++ b/Chapter1_Introduction/Ch1_Introduction_Pyro.ipynb @@ -588,7 +588,7 @@ "Pyro is a Python library for programming Bayesian analysis. It is intended for data scientists, statisticians, machine learning practitioners, and scientists. Since it is built on the PyTorch stack, it brings the runtime benefits of PyTorch to Bayesian analysis. These include write-once run-many (ability to run your development model in production) and speedups via state-of-the-art hardware (GPUs and TPUs). \n", "\n", "Since Pyro is relatively new, the Pyro community is actively developing documentation, \n", - "especially docs and examples that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why TFP is so cool.\n", + "especially docs and examples that bridge the gap between beginner and hacker. One of this book's main goals is to solve that problem, and also to demonstrate why Pyro is so cool.\n", "\n", "We will model the problem above using Pyro. This type of programming is called *probabilistic programming*, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random; it is probabilistic in the sense that we create probability models using programming variables as the model's components. \n", "\n",