mlbasicc's Introduction

Machine Learning Basics in C++

Neural networks are one of the most important methods in machine learning.

This project implements some of the widely used components of neural networks in C++ using Back Propagation and Stochastic Gradient Descent.

The result is a very basic kernel for machine learning, that can train a neural network with different neurons and cost functions.

The following cost functions are implemented:

The following neuron functions are implemented:

All parameters of the network can be configured from the command line.

As an example, the network trains the MNIST Dataset and achieves an accuracy of up to 98.5 %.

Documentation

Section	Content
Specification	describes the Math behind Neural Networks
Implementation	explains the Code
References	contains the list of books, websites, tools and third party libraries

Contributing

If you want to contribute to the project:

Write a specification, what you intend to achive, and what's the Math behind it.
Send the sepcification to me: [email protected].
Update the source code in a sandbox on your own system and test it for bugs.
Run the tests similar to my Test section in the documentation.

To Do

There is room for improvements:

I did not implement the code for validation data.
The data input is restricted to the IDX format as defined by Jann LeCun. CSV would be better.
The results are stored to a CSV file without any real useful structure. Maybe there are better ideas to store training results.
The network is fully connected in all layers. Convolutional networks should be better for some purposes.
Some matrix operations in backprop() and feedforward() may run faster if I could dig deeper into the eigen matrix code.

Recommend Projects