Stock Market Prediction Using Multi-Layer Perceptrons With TensorFlow

This post is part of a series on artificial neural networks (ANN) in TensorFlow and Python.

  1. Stock Market Prediction Using Multi-Layer Perceptrons With TensorFlow
  2. Stock Market Prediction in Python Part 2
  3. Visualizing Neural Network Performance on High-Dimensional Data
  4. Image Classification Using Convolutional Neural Networks in TensorFlow

In this post a multi-layer perceptron (MLP) class based on the TensorFlow library is discussed. The class is then applied to the problem of performing stock prediction given historical data. Note: This post is not meant to characterize how stock prediction is actually done; it is intended to demonstrate the TensorFlow library and MLPs.

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CombinoChord: A Guitar Chord Generator App

This post is concerned with an approach to generating guitar chords fingerings given run-time parameters regarding the guitar configuration and player’s hand. The approach is expected to run acceptably on an Android mobile device and should be responsive to user input and should assign conventional fingerings high heuristic scores. The core source code that is described in this post is available at the following git repository. The app is available for download on the Google play store.

Problem Significance

The non-trivial nature of this problem stems from the way in which guitars are constructed. A brute force approach is unsatisfactory because there are a large number of possible candidates the vast majority of which are anatomically impossible or produce incorrect notes. Consider enumerating every possible way in which a player could place his or her fingers (excluding the thumb). Due to the fact that each finger may optionally form a barre, the number of candidates to consider is:

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