fixed Pkg command in all docs

Author: Shravan Goswami

tutorials/00-introduction/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 ### Introduction

tutorials/01-gaussian-mixture-model/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 The following tutorial illustrates the use of Turing for clustering data using a Bayesian mixture model.

tutorials/02-logistic-regression/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 [Bayesian logistic regression](https://en.wikipedia.org/wiki/Logistic_regression#Bayesian) is the Bayesian counterpart to a common tool in machine learning, logistic regression.

tutorials/03-bayesian-neural-network/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 In this tutorial, we demonstrate how one can implement a Bayesian Neural Network using a combination of Turing and [Flux](https://github.com/FluxML/Flux.jl), a suite of machine learning tools. We will use Flux to specify the neural network's layers and Turing to implement the probabilistic inference, with the goal of implementing a classification algorithm.

tutorials/04-hidden-markov-model/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 This tutorial illustrates training Bayesian [Hidden Markov Models](https://en.wikipedia.org/wiki/Hidden_Markov_model) (HMM) using Turing. The main goals are learning the transition matrix, emission parameter, and hidden states. For a more rigorous academic overview on Hidden Markov Models, see [An introduction to Hidden Markov Models and Bayesian Networks](http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf) (Ghahramani, 2001).

tutorials/05-linear-regression/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 Turing is powerful when applied to complex hierarchical models, but it can also be put to task at common statistical procedures, like [linear regression](https://en.wikipedia.org/wiki/Linear_regression).

tutorials/06-infinite-mixture-model/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 In many applications it is desirable to allow the model to adjust its complexity to the amount of data. Consider for example the task of assigning objects into clusters or groups. This task often involves the specification of the number of groups. However, often times it is not known beforehand how many groups exist. Moreover, in some applictions, e.g. modelling topics in text documents or grouping species, the number of examples per group is heavy tailed. This makes it impossible to predefine the number of groups and requiring the model to form new groups when data points from previously unseen groups are observed.

tutorials/07-poisson-regression/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 This notebook is ported from the [example notebook](https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-poisson-regression.html) of PyMC3 on Poisson Regression.

tutorials/08-multinomial-logistic-regression/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 [Multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression) is an extension of logistic regression. Logistic regression is used to model problems in which there are exactly two possible discrete outcomes. Multinomial logistic regression is used to model problems in which there are two or more possible discrete outcomes.

tutorials/09-variational-inference/index.qmd

@@ -7,7 +7,7 @@ engine: julia
 #| echo: false
 #| output: false
 using Pkg;
-Pkg.instantiate(".");
+Pkg.instantiate();
 ```
 
 In this post we'll have a look at what's know as **variational inference (VI)**, a family of _approximate_ Bayesian inference methods, and how to use it in Turing.jl as an alternative to other approaches such as MCMC. In particular, we will focus on one of the more standard VI methods called **Automatic Differentation Variational Inference (ADVI)**.