Author Archives: jamesdmccaffrey

My First Impressions Of The fast.ai Library Were Not Good

The fast.ai library is a set of wrapper functions over the PyTorch code library. See ww.fast.ai. The idea is that PyTorch operates at a low level of abstraction and is quite difficult to learn and use. The fast.ai library provides … Continue reading

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Binary Classification with Logistic Regression Using ML.NET

I’ve been poking around the ML.NET code library. ML.NET is a C# library that can do classical machine learning (but not neural systems). ML.NET is a very large library and just like most things, it can only be learned by … Continue reading

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NFL 2019 Week 3 Predictions – Key Player Injuries Confuse Zoltar

Zoltar is my NFL prediction computer program. It uses a deep neural network and reinforcement learning. Here are Zoltar’s predictions for week #3 of the 2019 NFL season: Zoltar: titans by 1 dog = jaguars Vegas: titans by 1.5 Zoltar: … Continue reading

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Why Machine Learning One-Versus-All is Not a Good Technique

In machine learning, using the one-versus-all technique is almost never a good idea. One-versus-all (OvA) is also called one-versus-rest (OvR), and several other similar terms. A binary classification problem is one where the goal is to predict something that can … Continue reading

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PyTorch Tanh and a Downside to Open Source Software

I try to write a little bit of code each day. Writing code is a skill that can only be learned by practice, and furthermore, if you don’t practice you will lose your existing skill. I don’t speak any foreign … Continue reading

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A Recap of the 2019 CEC Conference

I spoke at the 2019 CEC (Casino/Cloud eSports Conference) event. The conference ran September 4-5 and was in Las Vegas. The conference was small (maybe about 150 people) but was a very good event from my perspective because both the … Continue reading

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Feature Engineering and Machine Learning

Suppose you want to predict a person’s annual income based on their number years of experience, age, number years education, and so on. In classical statistics it’s common to spend a lot of time on feature engineering — deciding which … Continue reading

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