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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Imaging as the Nidus of Precision Cerebrovascular Health

#Brain imaging, precision medicine, & the digital health revolution

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  • Other data types are undoubtedly informative the multidimensional array of data stems from imaging as the nidus.
  • Studying a million hearts may be important to prevent stroke the diverse manifestations of stroke occur in the brain, not the heart.
  • 4 – 6 Every aspect of cerebrovascular medicine currently emanates from phenotypic characterization with imaging.

This Viewpoint describes the importance of aggregating imaging data to help improve cerebrovacular health.
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