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| author | TinWoodman92 <chrhodgden@gmail.com> | 2023-12-03 20:13:35 -0600 |
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| committer | TinWoodman92 <chrhodgden@gmail.com> | 2023-12-03 20:13:35 -0600 |
| commit | 7bb1ec6c5cf46949697770d49caa3d7dce5ac21d (patch) | |
| tree | 4f77398b7323f2433979e84873887d96a7167471 /projects_blog/NNetwork.html | |
| parent | 9262b304e002b93cd270784fbfaf3a9d505773bf (diff) | |
added projects and NNetowrk page
Diffstat (limited to 'projects_blog/NNetwork.html')
| -rw-r--r-- | projects_blog/NNetwork.html | 66 |
1 files changed, 66 insertions, 0 deletions
diff --git a/projects_blog/NNetwork.html b/projects_blog/NNetwork.html new file mode 100644 index 0000000..9612ed8 --- /dev/null +++ b/projects_blog/NNetwork.html @@ -0,0 +1,66 @@ +<!DOCTYPE html> +<html lang="en"> +<head> + <title>chrhodgden - NNetwork</title> + <link rel="stylesheet" href="../style.css"/> + <script src="../app.js"></script> +</head> +<body> + + <h1>NNetwork</h1> + <h3>A simple neural network in R as an R6 class object</h3> + <a href="https://github.com/chrhodgden/NNetwork">GitHub Repository</a> + <h2>Background</h2> + <p> + After learning R from a few R tutorials, I decided it was time to learn what machine learning and data science truly was. + I had been putting off looking those up because I didn't feel like I was ready. + I watched a series on neural networks by 3Blue1Brown on YouTube linked below. + <br> + <br> + <a href="https://youtu.be/aircAruvnKk"> + But what is a neural network? | Chapter 1, Deep learning + </a> + <br> + <br> + <i>"Oh, I can do that."</i> I immediately thought to myself. + <br> + <br> + This video series applied old and familiar concepts of linear algebra and multivariable calculus that I had learned in college. + Knowing that there were applications of this with data and programming inspired me to try to write some libraries from scratch. + </p> + <h2>The Project</h2> + <p> + I chose R to do this rather than Python because I wanted to build experience with R. + This project would be a good demonstration of mixing higher mathmatics with programming, which is what R was built to do. + <br> + <br> + I did not follow any programming tutorials when developing this. + My primary intention was to familiarize myself with the math behind these concepts. + I wrote out as much of the program that made sense to me and then referenced YouTube for more detailed topics as I came to them. + I primariliy referenced a series by deeplizard on Neural Networks linked below. + <br> + <br> + <a href="https://www.youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU"> + Deep Learning Fundamentals - Intro to Neural Networks + </a> + <br> + <br> + This series was helpful and taught me about weights and bias initialization, the learning rate, and walked the tedious math sequences in a way that I could follow with my code. + <br> + <br> + One idea I came up with myself was simple testing process to verify the project worked. + I decided to test the network by training it to read binary. + This way I would not have to find or build a database of training data, nor label the data. + </p> + <h2>Next Steps</h2> + <p> + This project was written in December of 2022 and added to GitHub in February of 2023. + The next seps I would like to do would be to formalize the testing process into a Unit Test with the testthat library in R. + After that, I would like to format the library into a package that could be installed consistently into other machines. + Not necesarrily through CRAN, but through GitHub. + </p> + + <a href="../index.html">Home Page</a> + +</body> +</html>
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