Overview of the DeepLearning Package
The DeepLearning package is a collection of tools for machine learning. The package supports several common operations used with neural networks, including classification and regression.
Commands for Managing Tensors
Commands for Managing Dataflow Graphs
Commands for Managing Models
Commands for Constructing Estimators
Commands for Constructing Layers
Commands for Constructing Models
Commands for Constructing Feature Columns
Commands for Managing Sessions
DeepLearning makes use of the following custom types
The core object in a DeepLearning computation is a Tensor. The following commands construct Tensor objects in the active graph.
The following commands allow querying and modification of the DataflowGraph in which the current computation occurs.
The following commands construct executable versions of a Model object.
The following commands construct Estimator objects for classification and regression tasks.
The following commands construct Layer objects for classification and regression tasks.
The following commands construct Model objects for classification and regression tasks.
The following commands construct FeatureColumn objects for use with an Estimator.
The following commands manage Session objects.
The DeepLearning package is implemented using Google TensorFlow™ and provides access to a subset of the TensorFlow Python API, version 2.10.0.
macOS version 11.3 or later is required for Macs with Intel CPUs, and 12.5 or later for Apple Silicon Macs.
For Windows, a processor with AVX instructions is required. For more information, see the Release 1.6.0 section in https://github.com/tensorflow/tensorflow/blob/r1.10/RELEASE.md.
The DeepLearning package was introduced in Maple 2018.
For more information on Maple 2018 changes, see Updates in Maple 2018.
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