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.

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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.

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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.

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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.

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Introducing: Deep Learning Deathmatch TV

This post is made in introduction of a new YouTube series titled Deep Learning Deathmatch. In this series, a deep learning based AI that relies only on visual input is pitted against video game bosses. The resulting battle is recorded, edited, and compiled into a video. In the first video in the series, the AI fights the Oasis boss in Path of Exile.

More videos are available on my YouTube channel.

PoE AI Part 5: Real-Time Obstacle and Enemy Detection using CNNs in TensorFlow

This post is the fifth part of a series on creating an AI for the game Path of Exile © (PoE).

  1. A Deep Learning Based AI for Path of Exile: A Series
  2. Calibrating a Projection Matrix for Path of Exile
  3. PoE AI Part 3: Movement and Navigation
  4. PoE AI Part 4: Real-Time Screen Capture and Plumbing
  5. 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!

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Using Random Forests and Wordclouds to Visualize Feature Importance in Document Classification

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:

  • Austen
  • Dickens
  • Dostoyevsky
  • Doyle
  • Dumas
  • Stevenson
  • Stoker
  • Tolstoy
  • Twain
  • Wells

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