Saying that layer normalization normalizes input across the features was difficult for me to understand initially. Here's what made it click for me - straight from the Layer Normalization paper abstract:

"In this paper, we transpose batch normalization into layer normalization by **computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case**. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times."

https://arxiv.org/pdf/1607.06450.pdf


Google Test is a unit testing framework for C++. Let’s walk through how to get it installed and write some basic unit tests. Credits to Steven whose installation instructions I’ve condensed and added to here.

Part 1: Installation

Prerequisites:

  • Homebrew (run the following in your terminal):
xcode-select --installsudo xcodebuild -license accept

Then, restart your terminal and run:

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
  • CMake:
brew install cmake
  • Python, which is most likely already installed on your system (run python -h to check this)

These instructions assume you are installing to your home directory, but you can install it anywhere you choose. …

Alex Bussan

Machine Learning Engineer. Interested in how ML/AI can be applied to both business and art.

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