szha closed pull request #9834: minor spelling tweaks for docs
URL: https://github.com/apache/incubator-mxnet/pull/9834
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diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md
index 1a88242592..54ee334f97 100644
--- a/docs/tutorials/basic/data.md
+++ b/docs/tutorials/basic/data.md
@@ -416,7 +416,7 @@ data_iter = mx.io.ImageRecordIter(
data_shape=(3, 227, 227), # output data shape. An 227x227 region will be
cropped from the original image.
batch_size=4, # number of samples per batch
resize=256 # resize the shorter edge to 256 before cropping
- # ... you can add more augumentation options as defined in ImageRecordIter.
+ # ... you can add more augmentation options as defined in ImageRecordIter.
)
data_iter.reset()
batch = data_iter.next()
diff --git a/docs/tutorials/basic/image_io.md b/docs/tutorials/basic/image_io.md
index 8d60ee8fc0..092affbc74 100644
--- a/docs/tutorials/basic/image_io.md
+++ b/docs/tutorials/basic/image_io.md
@@ -85,7 +85,7 @@ data_iter = mx.io.ImageRecordIter(
data_shape=(3, 227, 227), # output data shape. An 227x227 region will be
cropped from the original image.
batch_size=4, # number of samples per batch
resize=256 # resize the shorter edge to 256 before cropping
- # ... you can add more augumentation options here. use
help(mx.io.ImageRecordIter) to see all possible choices
+ # ... you can add more augmentation options here. use
help(mx.io.ImageRecordIter) to see all possible choices
)
data_iter.reset()
batch = data_iter.next()
diff --git a/docs/tutorials/basic/record_io.md
b/docs/tutorials/basic/record_io.md
index e415d9448b..9ba6fa6e25 100644
--- a/docs/tutorials/basic/record_io.md
+++ b/docs/tutorials/basic/record_io.md
@@ -2,7 +2,7 @@
This tutorial will walk through the python interface for reading and writing
record io files. It can be useful when you need more more control over the
-details of data pipeline. For example, when you need to augument image and
label
+details of data pipeline. For example, when you need to augment image and label
together for detection and segmentation, or when you need a custom data
iterator
for triplet sampling and negative sampling.
@@ -16,7 +16,7 @@ import numpy as np
import matplotlib.pyplot as plt
```
-The relevent code is under `mx.recordio`. There are two classes: `MXRecordIO`,
+The relevant code is under `mx.recordio`. There are two classes: `MXRecordIO`,
which supports sequential read and write, and `MXIndexedRecordIO`, which
supports random read and sequential write.
diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md
index dbb1907bad..e10f3987ee 100644
--- a/docs/tutorials/gluon/customop.md
+++ b/docs/tutorials/gluon/customop.md
@@ -171,7 +171,7 @@ class DenseProp(mx.operator.CustomOpProp):
### Use CustomOp together with Block
-Parameterized CustomOp are ususally used together with Blocks, which holds the
parameter.
+Parameterized CustomOp are usually used together with Blocks, which holds the
parameter.
```python
diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
index 0bd616c369..fc2271999f 100644
--- a/docs/tutorials/gluon/mnist.md
+++ b/docs/tutorials/gluon/mnist.md
@@ -50,7 +50,7 @@ val_data = mx.io.NDArrayIter(mnist['test_data'],
mnist['test_label'], batch_size
## Approaches
-We will cover a couple of approaches for performing the hand written digit
recognition task. The first approach makes use of a traditional deep neural
network architecture called Multilayer Percepton (MLP). We'll discuss its
drawbacks and use that as a motivation to introduce a second more advanced
approach called Convolution Neural Network (CNN) that has proven to work very
well for image classification tasks.
+We will cover a couple of approaches for performing the hand written digit
recognition task. The first approach makes use of a traditional deep neural
network architecture called Multilayer Perceptron (MLP). We'll discuss its
drawbacks and use that as a motivation to introduce a second more advanced
approach called Convolution Neural Network (CNN) that has proven to work very
well for image classification tasks.
Now, let's import required nn modules
@@ -142,7 +142,7 @@ for i in range(epoch):
z = net(x)
# Computes softmax cross entropy loss.
loss = gluon.loss.softmax_cross_entropy_loss(z, y)
- # Backpropogate the error for one iteration.
+ # Backpropagate the error for one iteration.
ag.backward([loss])
outputs.append(z)
# Updates internal evaluation
diff --git a/docs/tutorials/python/mnist.md b/docs/tutorials/python/mnist.md
index 067ded96ab..e408ead5ae 100644
--- a/docs/tutorials/python/mnist.md
+++ b/docs/tutorials/python/mnist.md
@@ -44,7 +44,7 @@ val_iter = mx.io.NDArrayIter(mnist['test_data'],
mnist['test_label'], batch_size
```
## Training
-We will cover a couple of approaches for performing the hand written digit
recognition task. The first approach makes use of a traditional deep neural
network architecture called Multilayer Percepton (MLP). We'll discuss its
drawbacks and use that as a motivation to introduce a second more advanced
approach called Convolution Neural Network (CNN) that has proven to work very
well for image classification tasks.
+We will cover a couple of approaches for performing the hand written digit
recognition task. The first approach makes use of a traditional deep neural
network architecture called Multilayer Perceptron (MLP). We'll discuss its
drawbacks and use that as a motivation to introduce a second more advanced
approach called Convolution Neural Network (CNN) that has proven to work very
well for image classification tasks.
### Multilayer Perceptron
diff --git a/docs/tutorials/sparse/csr.md b/docs/tutorials/sparse/csr.md
index bbe71ff40c..e66b10d998 100644
--- a/docs/tutorials/sparse/csr.md
+++ b/docs/tutorials/sparse/csr.md
@@ -13,7 +13,7 @@ For matrices of high sparsity (e.g. ~1% non-zeros = ~1%
density), there are two
- memory consumption is reduced significantly
- certain operations are much faster (e.g. matrix-vector multiplication)
-You may be familiar with the CSR storage format in
[SciPy](https://www.scipy.org/) and will note the similarities in MXNet's
implementation. However there are some additional competitive features in
`CSRNDArray` inherited from `NDArray`, such as non-blocking asynchronous
evaluation and automatic parallelization that are not available in SciPy's
flavor of CSR. You can find further explainations for evaluation and
parallization strategy in MXNet in the [NDArray
tutorial](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html#lazy-evaluation-and-automatic-parallelization).
+You may be familiar with the CSR storage format in
[SciPy](https://www.scipy.org/) and will note the similarities in MXNet's
implementation. However there are some additional competitive features in
`CSRNDArray` inherited from `NDArray`, such as non-blocking asynchronous
evaluation and automatic parallelization that are not available in SciPy's
flavor of CSR. You can find further explanations for evaluation and
parallelization strategy in MXNet in the [NDArray
tutorial](https://mxnet.incubator.apache.org/tutorials/basic/ndarray.html#lazy-evaluation-and-automatic-parallelization).
The introduction of `CSRNDArray` also brings a new attribute, `stype` as a
holder for storage type info, to `NDArray`. You can query **ndarray.stype** now
in addition to the oft-queried attributes such as **ndarray.shape**,
**ndarray.dtype**, and **ndarray.context**. For a typical dense NDArray, the
value of `stype` is **"default"**. For a `CSRNDArray`, the value of stype is
**"csr"**.
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