On Forename Popularity in the USA

In this chapter, forenames in the USA are considered. The United States Social Security Administration (SSA) makes available a dataset containing information about Social Security records. The dataset contains counts of the number of records that exist for a specific first name and birth year.

Continue reading “On Forename Popularity in the USA”


A Statistical Analysis of Facial Attractiveness

An intermediate activation volume produced by a convolutional neural network predicting the attractiveness of a person.

Does beauty truly lie in the eye of its beholder? This chapter explores the complex array of factors that influence facial attractiveness to answer that question or at least to understand it better.

Continue reading “A Statistical Analysis of Facial Attractiveness”

Visualizing Bitcoin Wealth Distribution

This post explores the distribution of wealth among nonempty addresses on the Bitcoin network.

All addresses on the Bitcoin network are queried. The number of addresses with at least one satoshi is 24,473,765 at the time of the query. The resulting addresses are sorted by the amount of Bitcoin they contain. The list is divided into quantiles and the wealth of each quantile is plotted in a bar plot.

Continue reading “Visualizing Bitcoin Wealth Distribution”

Binary Classification with Artificial Neural Networks using Python and TensorFlow

This post is an introduction to using the TFANN module for classification problems. The TFANN module is available here on GitHub. The name TFANN is an abbreviation for TensorFlow Artificial Neural Network. TensorFlow is an open-source library for data flow programming. Due to the nature of computational graphs, using TensorFlow can be challenging at times. The TFANN module provides several classes that allow for interaction with the TensorFlow API using familiar object-oriented programming paradigms.

Continue reading “Binary Classification with Artificial Neural Networks using Python and TensorFlow”

Cryptocurrency Price Prediction Using Deep Learning in TensorFlow

In this post, deep learning neural networks are applied to the problem of predicting Bitcoin and other cryptocurrency prices. A chartist approach is taken to predict future values; the network makes predictions based on historical trends in the price and trading volume. A 1D convolutional neural network (CNN) transforms an input volume consisting of historical prices from several major cryptocurrencies into future price information.

Continue reading “Cryptocurrency Price Prediction Using Deep Learning in TensorFlow”

What is a Bitcoin Worth, Anyway?

Is the recent surge in Bitcoin’s price a speculative bubble?

By definition, an economic bubble is a situation in which an asset is traded within a price range that far exceeds its intrinsic value. So, the question is: what is the intrinsic value of Bitcoin? The purpose of this post is to explain some of the technical details of Bitcoin so as to gain a better idea of its value.

Continue reading “What is a Bitcoin Worth, Anyway?”

Deep Learning OCR using TensorFlow and Python

In this post, deep learning neural networks are applied to the problem of optical character recognition (OCR) using Python and TensorFlow. This post makes use of TensorFlow and the convolutional neural network class available in the TFANN module. The full source code from this post is available here.

Continue reading “Deep Learning OCR using TensorFlow and Python”