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The Wolfram Language has state-of-the-art capabilities for the construction, training and deployment of neural network machine learning systems. Many standard layer types are available and are assembled symbolically into a network, which can then immediately be trained and deployed on available CPUs and GPUs. Classify — automatic training and classification using neural networks and other methods.
Predict — automatic training and data prediction. FeatureExtraction — automatic feature extraction from image, text, numeric, etc.
LearnDistribution — automatic learning of data distribution. ImageIdentify — fully trained image identification for common objects. NetModel — complete pre-trained net models.
ResourceData — access to training data, networks, etc. NetGraph — symbolic representation of trained or untrained net graphs to be applied to data. NetChain — symbolic representation of a simple chain of net layers. NetPort — symbolic representation of a named input or output port for a layer. NetExtract — extract properties and weights etc.
NetInformation — give summary and detailed information about any network. NetTrain — train parameters in any net from examples.
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NetInitialize — randomly initialize parameters for a network. NetPortGradient — differentiate a net with respect to a port.
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NetStateObject — store and reuse recurrent state in a net. NetTrainResultsObject — represent what happened in net training. NetMeasurements — measure the performance of a net on test data. TrainingProgressMeasurements — measure performance metrics during training.
LinearLayer — trainable layer with dense connections computing. ElementwiseLayer — apply a specified function to each element in a tensor. SoftmaxLayer — layer globally normalizing elements to the unit interval. ConstantArrayLayer — embed a learned constant array into a NetGraph.
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EmbeddingLayer — trainable layer for embedding integers into continuous vector spaces. AttentionLayer — trainable layer for finding parts of a sequence to attend to. NetMapOperator — define a network that maps over a sequence.
NetMapThreadOperator — define a network that maps over multiple sequences.
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NetFoldOperator — define a recurrent network that folds in elements of a sequence. NetSharedArray — represent an array shared between several layers. NetInsertSharedArrays — convert all arrays in a net into shared arrays.
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NetEncoder — convert images, categories, etc. NetDecoder — interpret net-generated numerical arrays as images, probabilities, etc.
Ramp — rectified linear ReLU. ClassifierMeasurements — measure accuracy, recall, etc. DeleteMissing — remove missing data before training.
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