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Latest commit e9b29b3 Jul 10, 2017 @Jeff-Horton Jeff-Horton committed on GitHub Incorporated editing changes

Ryskamp Learning Machineanalytics



The Ryskamp Learning Machine represents a quantum leap in the world of machine learning. It breaks from the traditions of the past and uses a completely new paradigm for the core machine learning algorithm. This algorithm focuses on logic over pure mathematical solutions and specific information processing (currently associated with traditional programming) combined with categorization and pattern recognition (currently associated with machine learning) into a single algorithm and engine. Additionally, the RLM saves every decision it ever makes, making debugging simple. Neural network “black box” problems are now a thing of the past.

1. Exponentially faster calculation.

The Ryskamp Learning Machine uses a completely new algorithm for machine learning that breaks from the limitations of traditional machine learning algorithms. Hard to believe? Yes it is, which is why we invite you to download the source and try it yourself. Or you can test our speed against other engines on some of the applications on our challenge site.

2. Categorization, pattern recognition and specific memory in the same algorithm.

We invite you to try out our maze applications in the code on GitHub or on our challenge site. Notice how the Ryskamp Learning Machine only needs to complete the maze a single time to master it? Now compare the same algorithm running the lunar lander application. The maze demonstrates the use of specific memory (that is, remembering something after seeing it only once) while the lunar lander demonstrates that this can be combined with broader categorizations (i.e. using approximation for speeds and altitudes that vary much more than locations on a maze) in the same algorithm. These traits are critical in real-world environments where some specific situations require specific actions and other things can be dealt with in broader categories.

3. Substantially increased accuracy

In every challenge we have run, our engine has converged on a more accurate result in less time on every challenge against every other engine we tried. This quantum leap is hard to believe. So we open-sourced the code for evaluation and other non-commercial purposes. We invite you to try it on your own problem or challenge.

4. Solves a wider variety of problem sets than traditional machine learning.

The Ryskamp Learning Machine uses a different core algorithm. This algorithm’s inherent differences open it up to solving more types of problems. Traditional machine learning algorithms are good at pattern recognition, classification, and other things. Most engineers will use a combination of non-machine learning solutions enhanced by machine learning to solve complex problems that require solutions outside of the traditional “machine learning box”. The Ryskamp Learning Machine does not fit in this box. You can solve many types of problems with nothing more than the Ryskamp Learning Machine using its default settings. We encourage you to try it or contact us for more detailed information.

5. Uses a fraction of CPU/GPU hardware.

Unlike many traditional machine learning methods, the Ryskamp Learning Machine does not use heavy amounts of mathematics or statistics. Depending on the problem being solved, our use of the CPU or GPU is often less than 10% of the usage required by traditional machine learning to converge upon the same solution. There are two core reasons for this difference. First, we use more logic and less math in our core algorithm. This means less calculations are required. Second, our design is architected around today’s hardware. It uses a much more balanced approach to hardware allocation. This is simply a ramification of the algorithm being designed in a different age of hardware. Compare today’s hardware to the hardware available at the times when most advances of the neural network occurred. These networks still use many core concepts dating back to the Perceptron of the 1950’s.

6. Natively tracks every event for easy diagnostics.

The Ryskamp Learning Machine natively tracks every single decision it ever makes. This is part of what enables the specific memory discussed above. This is also a great diagnostic tool. Although traditional machine learning can be supplemented with logs or other tools, the Ryskamp Learning Machine natively tracks every action it ever takes. This allows for a completely new level of diagnostics. The “black box” of some traditional methods that cannot really explain “why” they made a decision are a thing of the past. With the Ryskamp Learning Machine simply use the SessionCaseHistory API to access every decision the machine has ever made. Note this could be limited if you choose to store less information for disk space conservation.

7. Configuration is simple.

In a traditional neural network, you must often configure many settings in order to match the problem you are solving. Settings like algorithms, number of neurons, layers of neurons, activation functions, inputs, outputs, and many other settings must be carefully considered to match the design of the network to the problem being solved. The Ryskamp Learning Machine is simple. There is only one algorithm and there are no activation functions. As with any network, users are required to set up inputs and outputs, and define the number of sessions to be run. A few other optional parameters are available but not required. We invite you to view a sample application in our source code to see how easy this process really is.

Developer Guide

We included a Developer Guide to get you started fast and easy. You can view it here.

Nuget Package

We now have our Nuget Package available. You may download it HERE or through the Nuget Package Manager on Visual Studio.

This is covered by the license here. One or more patents are still pending for the methods and systems disclosed in this code.