Fix grammatical mistakes in README

README.md

@@ -175,12 +175,12 @@ Convergence Delta: tensor([2.3842e-07, -4.7684e-07])
 The algorithm outputs an attribution score for each input element and a
 convergence delta. The lower the absolute value of the convergence delta the better
 is the approximation. If we choose not to return delta,
-we can simply not provide `return_convergence_delta` input
+we can simply not provide the `return_convergence_delta` input
 argument. The absolute value of the returned deltas can be interpreted as an
 approximation error for each input sample.
 It can also serve as a proxy of how accurate the integral approximation for given
 inputs and baselines is.
-If the approximation error is large, we can try larger number of integral
+If the approximation error is large, we can try a larger number of integral
 approximation steps by setting `n_steps` to a larger value. Not all algorithms
 return approximation error. Those which do, though, compute it based on the
 completeness property of the algorithms.
@@ -224,7 +224,7 @@ in order to get per example average delta.
 
 
 Below is an example of how we can apply `DeepLift` and `DeepLiftShap` on the
-`ToyModel` described above. Current implementation of DeepLift supports only
+`ToyModel` described above. The current implementation of DeepLift supports only the
 `Rescale` rule.
 For more details on alternative implementations, please see the [DeepLift paper](https://arxiv.org/abs/1704.02685).
 
@@ -286,7 +286,7 @@ In order to smooth and improve the quality of the attributions we can run
 to smoothen the attributions by aggregating them for multiple noisy
 samples that were generated by adding gaussian noise.
 
-Here is an example how we can use `NoiseTunnel` with `IntegratedGradients`.
+Here is an example of how we can use `NoiseTunnel` with `IntegratedGradients`.
 
 ```python
 ig = IntegratedGradients(model)
@@ -338,7 +338,7 @@ It is an extension of path integrated gradients for hidden layers and holds the
 completeness property as well.
 
 It doesn't attribute the contribution scores to the input features
-but shows the importance of each neuron in selected layer.
+but shows the importance of each neuron in the selected layer.
 ```python
 lc = LayerConductance(model, model.lin1)
 attributions, delta = lc.attribute(input, baselines=baseline, target=0, return_convergence_delta=True)