AI (Neural Network) Stock Selections for Forecast Period April 4 – April 29, 2022

This is the 3rd forecast release of the AI Neural Network stock performance forecast system – this is just a real-time test of the system and is not advice for purchasing of these stocks. I personally have not bought any of the stocks from any of forecasts (this one and the two preceding forecasts) asContinue reading “AI (Neural Network) Stock Selections for Forecast Period April 4 – April 29, 2022”

AI (Neural Network) Stock Selections for Forecast Period March 28 – April 25, 2022

This is not stock advice – this article is posted as proof that these stock selections were made before the start of the forecast period only. This is a real-time test of the AI (Neural Network) Stock Performance Forecast System. The first stock selection was the following (see image below from software file output): TheseContinue reading “AI (Neural Network) Stock Selections for Forecast Period March 28 – April 25, 2022”

Second Real-Time AI Stock Performance Prediction Selection Release for a 20-Trading-Day Period

This is the second prediction release (following up from the other post this morning). This is NOT advice to purchase these stocks – this is simply a way to post the predictions, and then see how the companies perform. The date / time stamp will serve as proof that the system made these predictions beforeContinue reading “Second Real-Time AI Stock Performance Prediction Selection Release for a 20-Trading-Day Period”

First Real-Time AI Stock Performance Prediction Selection Release for a 20-Trading-Day Period

I’ve been working to finish the code on the AI (Neural Network) stock performance prediction system which selects stocks that it deems highly likely to perform well over the next 20-trading-day period. There was a lot more detailed work than I expected when changing over from the logic for the yearly forecast to a monthlyContinue reading “First Real-Time AI Stock Performance Prediction Selection Release for a 20-Trading-Day Period”

Use Matlab API to Increase Performance of Java, C++, or Python Applications

MATLAB (https://www.mathworks.com/) – hereon referred to as “Matlab” – has come a long ways since the early days – especially regarding the execution time for matrix and other types of operations. It’s almost unparalleled when compared to the available math libraries for other coding languages. As such, it has become another important toolbox for theContinue reading “Use Matlab API to Increase Performance of Java, C++, or Python Applications”

Consulting for AI Projects

I’m currently open to consulting on AI projects – my AI resume can be reviewed here and my “Visual Resume” can be reviewed here. The best way to contact me is by email at first – mikescodeprojects@protonmail.com. Send me a description of what you want to accomplish and we can start discussing the issues aroundContinue reading “Consulting for AI Projects”

Neural Network Stock Selector

I’ve been developing this code base for about 6 years – even longer in a casual manner. Over the past 6 months I’ve been upgrading the code base from a very old version of Matlab to Matlab R2017b (4 years old but still reasonably recent). In a nutshell, the system develops Neural Networks to analyzeContinue reading “Neural Network Stock Selector”

What are Super Nets?

Let’s start with the example of students in a medical school. There are 1,000 students and the top 100 students (the top 10%) are getting straight A’s because they are bright and they have studied diligently. Would you say that all of these 100 students (the top 10 percenters) will do equally well out inContinue reading “What are Super Nets?”

A Software Profiler is Your Best Friend

One of the key assets in your suite of software testing tools is the Profiler, and you should get to know it well. The Profiler is “standard equipment” with most software development environments and has a wide array of capabilities to help point out weak areas of the code, to demonstrate the bottlenecks where mostContinue reading “A Software Profiler is Your Best Friend”

Good Data Means Fast Neural Network Training Times

The Pyrenn Levenberg-Marquardt training algorithm for Feed-Forward Neural Networks is extremely fast – 0.140 seconds for a Neural controller which must simultaneously balance an inverted pendulum, mounted on a cart, while moving the cart back to the origin – watch the short video below. In the vast majority of the Neural Network applications that I’veContinue reading “Good Data Means Fast Neural Network Training Times”