Learning Deep Learning with Keras

Learning #DeepLearning with #Keras  #NeuralNetworks @pmigdal

  • For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
  • Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
  • See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):

    In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.

  • Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
  • Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.

I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
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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.

Read the full article, click here.


@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#