Neural Nets in Azure ML – Introduction to Net#

Neural Nets in Azure ML – Introduction to Net#  #ai

  • The network has 3 layers of neurons: an input layer of size 28*28 = 784, one hidden layer of size 100, and the output layer of size 10.
  • You can easily add more layers resulting in a more complex neural network.
  • input Picture [28, 28]; // Note that alternatively we could declare input layer as: // input Picture [28 * 28]; // or just // input Picture [784]; // Net# compiler will be able to infer the number of dimensions automatically.
  • // This defines an output layer of size 10 which is fully-connected to layer ‘H’, // with softmax activation function.
  • The language also supports various types of layers which will be described in subsequent posts.

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@RickKing16: “Neural Nets in Azure ML – Introduction to Net# #ai”


Neural networks are one of the most popular machine learning algorithms today. One of the challenges when using neural networks is how to define a network topology given the variety of possible layer types, connections among them, and activation functions.  Net# solves this problem by providing a succinct way to define almost any neural network architecture in a descriptive, easy-to-read format. This post provides a short tutorial for building a neural network using the Net# language to classify images of handwritten numeric digits in Microsoft Azure Machine Learning. 


Neural Nets in Azure ML – Introduction to Net#