Minor FAQ Typo
Switch `to processes` -> `to process`.
@@ -125,7 +125,7 @@ Below are some common definitions that are necessary to know and understand to c
- *Example:* one image is a **sample** in a convolutional network
- *Example:* one audio file is a **sample** for a speech recognition model
- **Batch**: a set of *N* samples. The samples in a **batch** are processed independently, in parallel. If training, a batch results in only one update to the model.
- - A **batch** generally approximates the distribution of the input data better than a single input. The larger the batch, the better the approximation; however, it is also true that the batch will take longer to processes and will still result in only one update. For inference (evaluate/predict), it is recommended to pick a batch size that is as large as you can afford without going out of memory (since larger batches will usually result in faster evaluating/prediction).
+ - A **batch** generally approximates the distribution of the input data better than a single input. The larger the batch, the better the approximation; however, it is also true that the batch will take longer to process and will still result in only one update. For inference (evaluate/predict), it is recommended to pick a batch size that is as large as you can afford without going out of memory (since larger batches will usually result in faster evaluating/prediction).
- **Epoch**: an arbitrary cutoff, generally defined as "one pass over the entire dataset", used to separate training into distinct phases, which is useful for logging and periodic evaluation.
- When using `evaluation_data` or `evaluation_split` with the `fit` method of Keras models, evaluation will be run at the end of every **epoch**.
- Within Keras, there is the ability to add [callbacks](https://keras.io/callbacks/) specifically designed to be run at the end of an **epoch**. Examples of these are learning rate changes and model checkpointing (saving).
