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c++ program to get anchors needed for hand detection
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jaccard_coef_loss for keras. This loss is usefull when you have unbalanced classes within a sample such as segmenting each pixel of an image. For example you are trying to predict if each pixel is cat, dog, or background. You may have 80% background, 10% dog, and 10% cat. Should a model that predicts 100% background be 80% right, or 30%? Categor…
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This is a guide for Scala and Java development on Windows, using Windows Subsystem for Linux, although a bunch of it is applicable to a VirtualBox / Vagrant / Docker subsystem environment. This is not complete, but is intended to be as step by step as possible.
Harden Windows 10
Read the entire Decent Security guide, and follow the instructions, especially:
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Notes for "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper
The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.
Issues With Training Deep Neural Networks
Internal Covariate shift
Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.
It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.
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