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[Docs] corrected minor errors in the documentation #164

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[Docs] corrected minor errors in the documentation #164

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2 changes: 1 addition & 1 deletion docs/source/overview.md
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Expand Up @@ -46,7 +46,7 @@ architecture search to grow an ensemble of subnetworks:

Each AdaNet **iteration** has the given lifecycle:

![AdaNet iteration lifecucle](./assets/lifecycle.svg "The lifecycle of an AdaNet iteration.")
![AdaNet iteration lifecycle](./assets/lifecycle.svg "The lifecycle of an AdaNet iteration.")

Each of these concepts has an associated Python object:

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2 changes: 1 addition & 1 deletion docs/source/theory.md
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Expand Up @@ -43,7 +43,7 @@ rigorous manner.
learner's weight is inversely proportional to the Rademacher complexity of
its function class, and all the weights in the logits layer sum to 1.
Additionally, at training time, we don't have to discourage the trainer from
learning complex models -- it is only when we consider the how much the
learning complex models -- it is only when we consider how much the
model should contribute to the ensemble do we take the complexity of the
model into account.
* **Complexity is not just about the weights.** The Rademacher complexity of a
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