fix tradiational typo

Author: jorobledo

chapter_gaussian-processes/index.md

@@ -8,7 +8,7 @@ Gaussian processes (GPs) are ubitiquous. You have already encountered many examp
 
 Learning about Gaussian processes is important for three reasons: (1) they provide a _function space_ perspective of modelling, which makes understanding a variety of model classes, including deep neural networks, much more approachable; (2) they have an extraordinary range of applications where they are state-of-the-art, including active learning, hyperparameter learning, auto-ML, and spatiotemporal regression; (3) over the last few years, algorithmic advances have made Gaussian processes increasingly scalable and relevant, harmonizing with deep learning through frameworks such as [GPyTorch](https://gpytorch.ai) :cite:`Gardner.Pleiss.Weinberger.Bindel.Wilson.2018`. Indeed, GPs and deep neural networks are not competing approaches, but highly complementary, and can be combined to great effect. These algorithmic advances are not just relevant to Gaussian processes, but provide a foundation in numerical methods that is broadly useful in deep learning.
 
-In this chapter, we introduce Gaussian processes. In the introductory notebook, we start by reasoning intuitively about what Gaussian processes are and how they directly model functions. In the priors notebook, we focus on how to specify Gaussian process priors. We directly connect the tradiational weight-space approach to modelling to function space, which will help us reason about constructing and understanding machine learning models, including deep neural networks. We then introduce popular covariance functions, also known as _kernels_, which control the generalization properties of a Gaussian process. A GP with a given kernel defines a prior over functions. In the inference notebook, we will show how to use data to infer a _posterior_, in order to make predictions. This notebook contains from-scratch code for making predictions with a Gaussian process, as well as an introduction to GPyTorch. In upcoming notebooks, we will introduce the numerics behind Gaussian processes, which is useful for scaling Gaussian processes but also a powerful general foundation for deep learning, and advanced use-cases such as hyperparameter tuning in deep learning. Our examples will make use of GPyTorch, which makes Gaussian processes scale, and is closely integrated with deep learning functionality and PyTorch.
+In this chapter, we introduce Gaussian processes. In the introductory notebook, we start by reasoning intuitively about what Gaussian processes are and how they directly model functions. In the priors notebook, we focus on how to specify Gaussian process priors. We directly connect the traditional weight-space approach to modelling to function space, which will help us reason about constructing and understanding machine learning models, including deep neural networks. We then introduce popular covariance functions, also known as _kernels_, which control the generalization properties of a Gaussian process. A GP with a given kernel defines a prior over functions. In the inference notebook, we will show how to use data to infer a _posterior_, in order to make predictions. This notebook contains from-scratch code for making predictions with a Gaussian process, as well as an introduction to GPyTorch. In upcoming notebooks, we will introduce the numerics behind Gaussian processes, which is useful for scaling Gaussian processes but also a powerful general foundation for deep learning, and advanced use-cases such as hyperparameter tuning in deep learning. Our examples will make use of GPyTorch, which makes Gaussian processes scale, and is closely integrated with deep learning functionality and PyTorch.
 
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