Fixed links and other minor edits

Author: stephenchouca

sdh_documentation/docs/benchmarking.md

@@ -48,12 +48,12 @@ letting us measure only the performance of the computation itself.
     TACO's computational performance, since timing those functions will
     include the time it takes to generate code for performing the computation.
 
-**The time it takes to construct the initial input tensors should also not be
+**The time it takes to construct the initial operand tensors should also not be
 measured**, since again this overhead can often be amortized in practice.  By
 default, `pytaco.read` and functions for converting NumPy arrays and SciPy
 matrices to TACO tensors return fully constructed tensors.  If you add nonzero
-elements to an input tensor by calling `insert` though, then `pack` must also
-be explicitly invoked before any benchmarking is done:
+elements to an operand tensor by invoking its `insert` method though, then
+`pack` must also be explicitly invoked before any benchmarking is done:
 
 ```python
 import pytaco as pt

sdh_documentation/docs/data_analytics.md

@@ -9,7 +9,7 @@ $$A = B_{(1)} (D \odot C),$$
 where \(A\), \(C\), and \(D\) are typically dense matrices, \(B\) is a
 three-dimensional tensor (matricizied along the first mode), and \(\odot\)
 denotes the Khatri-Rao product. This operation can also be expressed in [index
-notation](computations.md#specifying-tensor-algebra-computations) as 
+notation](pycomputations.md#specifying-tensor-algebra-computations) as 
 
 $$A_{ij} = B_{ikl} \cdot D_{lj} \cdot C_{kj}.$$
 

sdh_documentation/docs/machine_learning.md

@@ -8,7 +8,7 @@ $$A = B \circ CD,$$
 where \(A\) and \(B\) are sparse matrices, \(C\) and \(D\) are dense matrices,
 and \(\circ\) denotes component-wise multiplication. This operation can also be
 expressed in [index
-notation](computations.md#specifying-tensor-algebra-computations) as 
+notation](pycomputations.md#specifying-tensor-algebra-computations) as 
 
 $$A_{ij} = B_{ij} \cdot C_{ik} \cdot C_{kj}.$$
 

sdh_documentation/docs/scientific_computing.md

@@ -6,7 +6,7 @@ $$y = Ax + z,$$
 
 where \(A\) is a sparse matrix and \(x\), \(y\), and \(z\)
 are dense vectors. The computation can also be expressed in [index
-notation](computations.md#specifying-tensor-algebra-computations) as 
+notation](pycomputations.md#specifying-tensor-algebra-computations) as 
 
 $$y_i = A_{ij} \cdot x_j + z_i.$$