Image Recognition Using Neural Networks

Saturday, November 8, 2008

The original post has been migrated from Excel/XLMiner, migrated from http://glue.umd.edu/~mmallari/ (sunsetted), and refreshed with newer datasets and references.

Artificial neural networks (ANN, and neural networks, for short) are powerful models inspired by the human brain, capable of learning complex classification patterns and making accurate predictions (Shmueli et al., 2007). Their significance lies in their ability to handle high-dimensional data, extract features automatically, and capture non-linear relationships.

Neural networks have found applications in various domains, including image and speech recognition, natural language processing, recommendation systems, and time series forecasting. The ability to model intricate relationships and leverage deep learning architectures has made neural networks indispensable in solving challenging problems and advancing the field of artificial intelligence (or AI).

Let’s apply this technique to the X dataset.

Getting Started

If you are interested in reproducing this work, here are the versions of R, Python, and Julia used (as well as the respective packages for each). Additionally, Leland Wilkinson’s approach to data visualization (Grammar of Graphics) has been adopted for this work. Finally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me.

cat(R.version$version.string, R.version$nickname)

R version 4.2.3 (2023-03-15) Shortstop Beagle

require(devtools)
devtools::install_version("dplyr", version="1.1.2", repos="http://cran.us.r-project.org")
devtools::install_version("ggplot2", version="3.4.2", repos="http://cran.us.r-project.org")

library(dplyr)
library(ggplot2)

Python

import sys
print(sys.version)

3.11.4 (v3.11.4:d2340ef257, Jun  6 2023, 19:15:51) [Clang 13.0.0 (clang-1300.0.29.30)]

!pip install pandas==2.0.3
!pip install plotnine==0.12.1

import pandas
import plotnine

Julia

using InteractiveUtils
InteractiveUtils.versioninfo()

Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin22.4.0)
  CPU: 8 × Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
  Threads: 1 on 8 virtual cores
Environment:
  DYLD_FALLBACK_LIBRARY_PATH = /Library/Frameworks/R.framework/Resources/lib:/Library/Java/JavaVirtualMachines/jdk1.8.0_241.jdk/Contents/Home/jre/lib/server

using Pkg
Pkg.add(name="CSV", version="0.10.11")
Pkg.add(name="DataFrames", version="1.5.0")
Pkg.add(name="CategoricalArrays", version="0.10.8")
Pkg.add(name="Colors", version="0.12.10")
Pkg.add(name="Cairo", version="1.0.5")
Pkg.add(name="Gadfly", version="1.3.4")

using Dates
using CSV
using DataFrames
using CategoricalArrays
using Colors
using Cairo
using Gadfly

Importing and Examining Dataset

References

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1
Shmueli, G., Patel, N. R., & Bruce, P. C. (2007). Data Mining for Business Intelligence. Wiley.
Albright, S. C., Winston, W. L., & Zappe, C. (2003). Data Analysis for Managers with Microsoft Excel (2^nd ed.). South-Western College Publishing.

Applied Advanced Analytics & AI in Sports