Category Archives: Machine Learning

Neural Binary Classification Using PyTorch

Of the neural network code libraries I use most often (TensorFlow, Keras, CNTK, PyTorch), PyTorch is by far the least mature. The Windows version of PyTorch was released only a few weeks ago. So, there are almost no good PyTorch … Continue reading

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NFL 2018 Week 3 Predictions – Zoltar Likes Underdogs Jets, Bills, Redskins, and Cardinals

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 2018 NFL season: Zoltar: jets by 1 dog = browns Vegas: browns by 3 Zoltar: … Continue reading

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Managing Neural Network Library Versions

I regularly use four neural network code libraries: TensorFlow, Keras, PyTorch, and CNTK. The libraries require Python. Managing all the different versions of these libraries is an annoying detail that has to be taken care of very carefully. I recently … Continue reading

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NFL 2018 Week 2 Predictions – Zoltar Likes Five Vegas Underdogs

Zoltar is my NFL prediction computer program. It uses a deep neural network and Reinforcement Learning. Here are Zoltar’s predictions for week #2 of the 2018 NFL season: Zoltar: ravens by 0 dog = bengals Vegas: ravens by 0 Zoltar: … Continue reading

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Yet Even More About PyTorch Neural Network Weight Initialization

I’ve been working through the details of the PyTorch neural network library. I’m still examining basic concepts like weight and bias initialization. Even a task as simple as setting weights to some fixed value is surprisingly tricky. Here’s example code … Continue reading

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More Fantasy Football and Machine Learning

I have this vague notion that there must be interesting connections between fantasy football and machine learning. I know a lot about machine learning but not a whole lot about fantasy football. So, several days ago I set up a … Continue reading

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Calculating Gini Impurity Example

The Gini Impurity (GI) metric measures the homogeneity of a set of items. GI can be used as part of a decision tree machine learning classifier. The lowest possible value of GI is 0.0. The maximum value of GI depends … Continue reading

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