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Diffstat (limited to 'nerv/doc/source/overview.rst')
| -rw-r--r-- | nerv/doc/source/overview.rst | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/nerv/doc/source/overview.rst b/nerv/doc/source/overview.rst index 8ff1dd5..5c75bd8 100644 --- a/nerv/doc/source/overview.rst +++ b/nerv/doc/source/overview.rst @@ -6,7 +6,7 @@ What is NERV? NERV is a general-purpose deep learning toolkit designed to be be simple, lightweight, extensible and complete. The name "NERV" comes from the German -word "nerv" for "nerve" in English. It is also a fictional organization in the +word "nerv" for "nerve" in English. It is also a fictional organization in *Neon Genesis Evangelion*, a famous anime. @@ -18,7 +18,8 @@ and related deep learning techniques have given rise to many tools and toolkits which are designed for constructing and training neural networks which could facilitate and routinize the research cycle of deep learning applied to areas such as speech processing, computer vision, natural language processing and so -on. Such tools or toolkits can be categorized into two by design: task-specific or general-purpose. +on. Such tools or toolkits can be categorized into two by design: task-specific +or general-purpose. The first category tries to address the deep learning in a direct way. These tools usually have a very specific goal, which means to support a certain type @@ -245,7 +246,7 @@ Network ******* Layers alone only declare the structure and behavior of the intended network. -To turn a layer into a trainable model, we need to *compile* the layer into an +To turn a layer into a trainable model, we need to *compile* the layer into a *network* instance (``nerv.Network``). What compilation does is to flatten out the nested graph layer according to the specified structure and allocate the space for those intermediate activations between the layers. Although the @@ -285,7 +286,7 @@ samples together and cut samples into mini-batches. Scheduler (Trainer) ******************* -*Schedulers* refer to those top-level scripts that implements the main training +*Schedulers* refer to those top-level scripts that implement the main training loop and ticks the training process. A general-purpose scheduler typically takes in a Lua script written by an end user that contains description of the network, task-specific processing for reading data and some hyper-parameters. |