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Can you write a neural network in C?
As part of delving deeper into machine learning concepts, I decided to write a simple neural network from scratch in C, without the help of any vector or matrix libraries. “Why C and no vector or matrix libraries?…”
How does a C O mplete neural network work?
The operation of a c o mplete neural network is straightforward : one enter variables as inputs (for example an image if the neural network is supposed to tell what is on an image), and after some calculations, an output is returned (following the first example, giving an image of a cat should return the word “cat”).
How to create a neural network from scratch?
But first, create two files (NeuralNetwork.cpp and NeuralNetwork.hpp) and write the above NeuralNetwork class code yourself in the “NeuralNetwork.hpp”. The following line of code must be copied in the “NeuralNetwork.cpp” file.
What was the first neural network ever created?
Okay, we know the basics, let’s check about the neural network we will create. The one explained here is called a Perceptron and is the first neural network ever created. It consists on 2 neurons in the inputs column and 1 neuron in the output column. This configuration allows to create a simple classifier to distinguish 2 groups.
What should be included in simple neural network implementation?
Although this code was not written with extensibility in mind, it would be interesting to expand it’s capabilities to include things such as : configurable number of hidden layers, batch gradient descent, and additional options for activation and error functions.
How to calculate the output of a neural network?
First portion is to calculate the output of the network given the current weights starting with calculating the hidden layer activation according to this formula: where j is the number of hidden nodes (2) and k is the number of inputs (2):
Which is the best library for neural networks?
Most sample neural networks posted online are written in Pytho n and use powerful math libraries such as numpy. While the code in these samples is clean and succinct, it can be hard to grasp the details behind back-propagation when complex matrix operations are collapsed into a single statement.