@@ -141,9 +141,12 @@ and then cast it to int.
141141.. note::
142142
143143 If you are running your code on the GPU and the dataset you are using
144- is too large to fit in memory the code will crash. In such a case, do
145- not store the data in a shared variable. You can however copy a larger chunk
146- of it at once (several minibatches) to reduce the overhead of data transfer.
144+ is too large to fit in memory the code will crash. In such a case you
145+ should store the data in a shared variable. You can however store a
146+ sufficiently small chunk of your data (several minibatches) in a shared
147+ variable and use that during trianing. One you got through the chunk,
148+ update the values it stores. This way you minimize the number of data
149+ transfers between CPU memory and GPU memory.
147150
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149152
@@ -170,7 +173,7 @@ use superscripts to distinguish training set examples: :math:`x^{(i)} \in
170173\mathcal{R}^D` is thus the i-th training example of dimensionality :math:`D`. Similarly,
171174:math:`y^{(i)} \in \{0, ..., L\}` is the i-th label assigned to input
172175:math:`x^{(i)}`. It is straightforward to extend these examples to
173- :math:`y^{(i)}` that has other types (e.g. Gaussian for regression,
176+ ones where :math:`y^{(i)}` has other types (e.g. Gaussian for regression,
174177or groups of multinomials for predicting multiple symbols).
175178
176179.. index:: Math Convetions
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