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.
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.
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.
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.
What characteristics do the works of famous authors have that make them unique? This post uses ensemble methods and wordclouds to explore just that.
Project Gutenberg offers a large number of freely available works from many famous authors. The dataset for this post consists of books, taken from Project Gutenberg, written by each of the following authors:
This post explores historical weather data from Los Angeles, California over the period of 1906 to the present using Pandas and Matplotlib. The data in the post was collected from the National Centers for Environmental Information website. An order must be placed through the website to obtain a (temporary) link to download the data.
This post is part of a series on artificial neural networks (ANN) in TensorFlow and Python.
- Stock Market Prediction Using Multi-Layer Perceptrons With TensorFlow
- Stock Market Prediction in Python Part 2
- Visualizing Neural Network Performance on High-Dimensional Data
- Image Classification Using Convolutional Neural Networks in TensorFlow
This post revisits the problem of predicting stock prices based on historical stock data using TensorFlow that was explored in a previous post. In the previous post, stock price was predicted solely based on the date. First, the date was converted to a numerical value in LibreOffice, then the resulting integer value was read into a matrix using numpy. As stated in the post, this method was not meant to be indicative of how actual stock prediction is done. This post aims to slightly improve upon the previous model and explore new features in tensorflow and Anaconda python. The corresponding source code is available here.