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.
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.
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.
This post is the fifth part of a series on creating an AI for the game Path of Exile © (PoE).
- A Deep Learning Based AI for Path of Exile: A Series
- Calibrating a Projection Matrix for Path of Exile
- PoE AI Part 3: Movement and Navigation
- PoE AI Part 4: Real-Time Screen Capture and Plumbing
- AI Plays Path of Exile Part 5: Real-Time Obstacle and Enemy Detection using CNNs in TensorFlow
As discussed in the first post of this series, the AI program takes a screenshot of the game and uses it to form predictions that are then used to update its internal state. In this post, methods for classifying and organizing information from visual input of the game screen is discussed. I have made the source code for this project available on my GitHub. Enjoy!