Category Archives: Machine Learning

Implementing k-Means Clustering using Python

I’m investigating the possibility of writing code for k-means clustering using the CNTK library. CNTK was designed to create deep neural networks. But CNTK has low-level functions that in principle will allow me to write code for clustering. The idea … Continue reading

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Min-Max Data Normalization with Python

I coded up a Python function that does min-max normalization on data stored in an array-of-arrays style matrix. I’ll explain why I did this shortly. Suppose you have height and weight data for a group of people. For example: 65.0, … Continue reading

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Finding Datasets for Machine Learning Explorations

I am currently writing a short (100 pages) e-book. The topic is the CNTK code library for deep neural networks. I spent a lot of time looking through the primary repository for students and researchers – the University of California … Continue reading

Posted in Machine Learning

NFL 2017 Week 18 (Wild Card) Predictions – Zoltar Likes Underdogs Falcons and Bills

Zoltar is my NFL football machine learning prediction system. It’s a hybrid system that uses a custom reinforcement learning algorithm plus a neural network. Here are Zoltar’s predictions for week #18 (the Wild Card games) of the 2017 NFL season: … Continue reading

Posted in Machine Learning, Zoltar

Poisson Regression

Poisson regression is a relatively rare classical statistics technique. I almost never use Poisson regression because in most cases, in my opinion, a neural network creates a better prediction model. The goal of a Poisson regression problem is to predict … Continue reading

Posted in Machine Learning

Time Series Regression with an LSTM

Time series regression problems — predicting the next value in a sequence — look simple but are almost always extremely difficult. There are many different techniques you can use to tackle a time series regression problem. One of the newest … Continue reading

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Fetching a Sequence of Items

I’m working on a time series regression problem. I’m experimenting with generating training sequences on the fly instead of using redundant data storage. Suppose you have 9 items: 10, 11, 12, 13, 14, 15, 16, 17, 18. If you use … Continue reading

Posted in Machine Learning