Neural Networks

Neural Networks in Mathematica 11

Mathematica Version 11 introduces a high-performance neural network framework with both CPU and GPU training support. A full complement of vision-oriented layers is included, as well as encoders and decoders to make trained networks interoperate seamlessly with the rest of the language. Constructing and training networks often requires only a few lines of code, putting deep learning in the hands of even nonexpert users.

Key Features

  • Use a wide range of image-oriented layer types to implement cutting-edge computer-vision algorithms. »
  • Define network topologies with multiple inputs, outputs, and arbitrary directed acyclic graph connectivity structure. »
  • Work with image, categorical, and numeric inputs and outputs. »
  • Define networks with multiple loss functions to perform multitask learning. »
  • Easily evaluate trained networks using a variety of built-in classifier metrics. »
  • Train on out-of-core image datasets. »
  • Train networks on either CPUs or NVIDIA GPUs.»
  • Take advantage of the NVIDIA CUDA® Deep Neural Network library (cuDNN) for optimal GPU performance. »
  • Import and export trained networks as "WLNet" files. »
  • Employ automatic tensor shape inference to write succinct network definitions. »

Related Examples

Digit Classification »

Unsupervised Learning with Autoencoders »

Object Classification»

Learn a Parameterization of a Manifold »

Learn to Classify Points from Different Clusters »

Out-of-Core Image Classification »

Multi-task Learning »

Use a Validation Set to Minimize Overfitting »

Measure Classification Performance »

Accelerate Training Using a GPU »

Generate Random Images »

Model an Image as a Function »