Titanic: Machine Learning from Disaster

For those of you who are not familiar with Kaggle.com, the latter website is a competition website concerned with data science and Machine Learning problems. Several commercial and non-profit companies and organization make their problems open to the public domain in hopes that they can find solutions to their own data problems or improve the performance of their existing ones. 

No matter what your level of expertise is with machine learning, there are many beginner-level problems to learn from and solve. There are also several real-world competitive problems that if solved efficiently would grant the winner teams up to 100,000$ as well as reputation points on the leaderboard. It is also a great source for learning machine learning approaches to solving problems. For more information on kaggle, I suggest browsing their homepage for more details.

The Hopfield Neural Network II

In this implementation of the Hopfield Neural Network, I designed the classes in a way that would make the network dynamic. By that I mean that the user can determine the size of the network during run-time of the program. Note that this post is a subsequent post to the original The Hopfield Neural Network I. Please refer to that post if you want to understand the internals of HNN.

Hopfield Neural Network

The Hopfield 4 Neurons Single Layer Neural Network

One of the simplest Neural Networks out there. Unlike other more complex Neural Nets, HNN employes a single network layer which is the input and output layer at the same time. This implementation of HNN contains four neurons acting together to recognize any four-digit binary pattern.

When the network is first initialized, it needs to be trained to recognize a specific binary pattern. Depending on the pattern we choose, the system will build what is called a weight matrix - during the training -. This weight matrix will help recognize the specific pattern we chose. When we feed the system with a new pattern and run the network, it will run the new pattern against the weight matrix and identify the pattern if it was the same original pattern or its inverse. Moreover, if the pattern was closely similar it will try to auto-correct the entered pattern. This is the auto-associative property of the Hopfield NN.