Convolutional Neural Nets

Published

November 20, 2024

Modified

May 17, 2025

Inspiration

Convolutional Neural Networks

What is Convolution?

Consider that you life in a high rise apartment complex. Have you heard an ambulance go by? How does the sound of the siren change as the ambulance approaches towards your dwelling and then goes past it to get lost amidst the surrounding buildings again?

The siren’s emitted sound is always the same. It is the local surroundings and the geometry of the echoes that brings the same sound to your ears again and again, but in altered form. The sound from the ambulance goes all around, hits on or other of the buildings, reflects, and comes back to your ears after a delay and weighted by the strength of the echo geometry.

You might consider that a CNN has several such echo mechanisms operating. Each pixel value (if you are dealing with image input) goes through a series of such delayed weightings which multiply the pixel input. The output of each pixel contributes in such fashion to the activation of that unit/layer of the CNN.

How does a CNN Structure use “Convolution”?

Let us contemplate the structure of a CNN.

Videos

Convolutional Neural Nets in Code

Using torch.

Wait, But Why?

References

  1. Digit Recognition with CNNs. Interactive! https://transcranial.github.io/keras-js/#/mnist-cnn
  2. CNN Convoluter. https://pwwang.github.io/cnn-convoluter/
  3. CNN Explainer: Learn Convolutional Neural Network (CNN) in your browser!. https://poloclub.github.io/cnn-explainer/
  4. Deep Lizard. Understanding Convolution Operations in Neural Networks. https://deeplizard.com/resource/pavq7noze2
  5. Colah’s Blog.(Apr 6, 2014). Neural Networks, Manifolds, and Topology. https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/. Very simple and readable article.
  6. Andrej Karpathy. ConvNetJS:Deep Learning in your browser.https://cs.stanford.edu/people/karpathy/convnetjs/
  7. https://developers.google.com/machine-learning.https://developers.google.com/machine-learning
  8. Adit Deshpande. A Beginner’s Guide to CNNs. https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/
  9. The Neural Network Zoo - The Asimov Institute. http://www.asimovinstitute.org/neural-network-zoo/
  10. It’s just a linear model: neural networks edition. https://lucy.shinyapps.io/neural-net-linear/
  11. Machine Learning Tokyo: Interactive Tools for ML/DL, and Math. https://github.com/Machine-Learning-Tokyo/Interactive_Tool
  12. Anyone Can Learn AI Using This Blog. https://colab.research.google.com/drive/1g5fj7W6QMER4-03jtou7k1t7zMVE9TVt#scrollTo=V8Vq_6Q3zivl
  13. Practical Deep Learning for Coders: An Online Free Course.https://course.fast.ai
  14. Neural Networks Visual with vcubingx
  1. The Neural Network Zoo - The Asimov Institute. http://www.asimovinstitute.org/neural-network-zoo/
  2. It’s just a linear model: neural networks edition. https://lucy.shinyapps.io/neural-net-linear/
  3. Neural Network Playground. https://playground.tensorflow.org/
  4. Rohit Patel (20 Oct 2024). Understanding LLMs from Scratch Using Middle School Math: A self-contained, full explanation to inner workings of an LLM. https://towardsdatascience.com/understanding-llms-from-scratch-using-middle-school-math-e602d27ec876
  5. Machine Learning Tokyo: Interactive Tools for ML/DL, and Math. https://github.com/Machine-Learning-Tokyo/Interactive_Tool
  6. Anyone Can Learn AI Using This Blog. https://colab.research.google.com/drive/1g5fj7W6QMER4-03jtou7k1t7zMVE9TVt#scrollTo=V8Vq_6Q3zivl
  7. Practical Deep Learning for Coders: An Online Free Course.https://course.fast.ai

Text Books

  1. Michael Nielsen. Neural Networks and Deep Learning, a free online book. https://neuralnetworksanddeeplearning.com/index.html
  2. Simone Scardapane. (2024) Alice’s Adventures in a differentiable Wonderland. https://www.sscardapane.it/alice-book/
  3. Parr and Howard (2018). The Matrix Calculus You Need for Deep Learning.https://arxiv.org/abs/1802.01528
  4. The Little Book of Deep Learning. Available Online
  5. Parr and Howard (2018). The Matrix Calculus You Need for Deep Learning.https://arxiv.org/abs/1802.01528
  6. Zhang, Lipton, Li, Smola. Dive into Deep Learning. https://www.d2l.ai/

LLMs

  1. Brendan Bycroft.Visualizing LLMs. https://bbycroft.net/llm
  2. Rohit Patel (20 Oct 2024). Understanding LLMs from Scratch Using Middle School Math: A self-contained, full explanation to inner workings of an LLM. https://towardsdatascience.com/understanding-llms-from-scratch-using-middle-school-math-e602d27ec876
  3. AI-powered reporting and annotation for radiology. https://www.md.ai
  4. Carl T. Bergstrom and Jevin D. West. Modern-Day Oracles or Bullshit Machines? https://thebullshitmachines.com/index.html

Using R for DL

  1. Geeks for Geeks. Convolutional Neural Nets in R. https://www.geeksforgeeks.org/convolutional-neural-networks-cnns-in-r/
  2. David Selby (9 January 2018). Tea and Stats Blog. Building a neural network from scratch in R. https://selbydavid.com/2018/01/09/neural-network/
  3. torch for R: An open source machine learning framework based on PyTorch. https://torch.mlverse.org
  4. Akshaj Verma. (2020-07-24). Building A Neural Net from Scratch Using R - Part 1 and Part 2. https://rviews.rstudio.com/2020/07/20/shallow-neural-net-from-scratch-using-r-part-1/ and https://rviews.rstudio.com/2020/07/24/building-a-neural-net-from-scratch-using-r-part-2/
  5. Ander Fernandez Jauregui. https://anderfernandez.com/en/blog/how-to-create-neural-networks-with-torch-in-r/
  6. https://f0nzie.github.io/rtorch-minimal-book/
  7. torch for R: An open source machine learning framework based on PyTorch. https://torch.mlverse.org/
R Package Citations
Package Version Citation
keras 2.15.0 @keras
safetensors 0.1.2 @safetensors
tensorflow 2.16.0 @tensorflow
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