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Co-authored-by: Boris De Vos <143942306+borisdevos@users.noreply.github.com>

Author: lkdvos

docs/src/index.md

@@ -19,7 +19,7 @@ By employing concepts from category theory, we can represent and manipulate tens
 At the same time, TensorKit.jl focusses on computational efficiency and performance.
 The underlying storage of a tensor's data can be any `DenseArray`.
 When the data is stored in main memory (corresponding to `Array`), multiple CPUs can be leveraged as many operations come with multithreaded implementations, either by distributing the different blocks in case of a structured tensor (i.e. with symmetries) or by using multithreading provided by the package [Strided.jl](https://github.com/Jutho/Strided.jl).
-Support for storing and manipulating tensors on NVidia and AMD GPUs is currently being developed, whereas support for distributed arrays is planned for the future.
+Support for storing and manipulating tensors on Nvidia and AMD GPUs is currently being developed, whereas support for distributed arrays is planned for the future.
 
 ## Contents of the manual
 

docs/src/man/tensors.md

@@ -17,7 +17,7 @@ This convention is opposite to the mathematical notation, e.g. ``\mathrm{Hom}(W,
 This also explains why we have consistently used the symbol ``W`` for spaces in the domain and ``V`` for spaces in the codomain.
 A tensor map ``t : (W_1 ⊗ … ⊗ W_{N_2}) → (V_1 ⊗ … ⊗ V_{N_1})`` will be created in Julia as `TensorMap(..., V1 ⊗ ... ⊗ VN₁, W1 ⊗ ... ⊗ WN₂)`.
 
-Furthermore, the abstract type `AbstractTensor{S, N}` is just a synonym for `AbstractTensorMap{S, N, 0}`, i.e. for tensor maps with an empty domain, which is equivalent to the unit of the monoidal category, or thus, the field of scalars ``𝕜``.
+Furthermore, the abstract type `AbstractTensor{T, S, N}` is just a synonym for `AbstractTensorMap{T, S, N, 0}`, i.e. for tensor maps with an empty domain, which is equivalent to the unit of the monoidal category, or thus, the field of scalars ``𝕜``.
 
 Currently, `AbstractTensorMap` has three subtypes.
 `TensorMap` provides the actual implementation, where the data of the tensor is stored in a `DenseArray` (more specifically a `DenseMatrix` as will be explained below).