fhieber commented on a change in pull request #7395: Drafted documentation for 
autograd. 
URL: https://github.com/apache/incubator-mxnet/pull/7395#discussion_r132286027
 
 

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 File path: docs/api/python/autograd.md
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 @@ -9,6 +9,64 @@
 .. warning:: This package is currently experimental and may change in the near 
future.
 ```
 
+## Overview
+
+The `autograd` package enables automatic
+differentiation of NDArray operations.
+In machine learning applications,
+`autograd` is often used to calculate the gradients
+of loss functions with respect to parameters.
+
+
+### Record vs Pause
+
+`autograd` records computation history on the fly to calculate gradients later.
+This is only enabled inside a `with autograd.record():` block.
+A `with auto_grad.pause()` block can be used inside a `record()` block
+to temporarily disable recording.
+
+To compute gradient with respect to an `NDArray` `x`, first call 
`x.attach_grad()`
+to allocate space for the gradient. Then, start a `with autograd.record()` 
block,
+and do some computation. Finally, call `backward()` on the result:
+
+```python
+>>> x = mx.nd.array([1,2,3,4])
+>>> x.attach_grad()
+>>> with mx.autograd.record():
+...     y = x * x + 1
+>>> print(x.grad)
+[ 2.  4.  6.  8.]
+<NDArray 4 @cpu(0)>
+```
+
+
+## Train mode and Predict Mode
+
+Some operators (Dropout, BatchNorm, etc) behave differently in
 
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