https://github.com/suryanarayanaa/quantgrad

A Small(Quant) Auto Grad Library with visualization of individual operations within Neurons
https://github.com/suryanarayanaa/quantgrad

autodifferentiation autograd autograder deep-learning deep-neural-networks visualization

Last synced: 1 day ago
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A Small(Quant) Auto Grad Library with visualization of individual operations within Neurons

• Host: GitHub
• URL: https://github.com/suryanarayanaa/quantgrad
• Owner: SuryaNarayanaa
• Created: 2025-01-16T13:21:49.000Z (28 days ago)
• Default Branch: main
• Last Pushed: 2025-02-11T18:08:20.000Z (1 day ago)
• Last Synced: 2025-02-11T19:24:41.840Z (1 day ago)
• Topics: autodifferentiation, autograd, autograder, deep-learning, deep-neural-networks, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 1.28 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project README

This project contains multiple sub-projects. You can find their respective README files below:

- [Derivatives README](./Derivatives/README.md)

- [QuantGrad README](./QuantGrad/README.md)

- This is how a 100 parameter FC NN looks like, pretty dope

![This is how a 100 parameter FC NN looks like](./This%20is%20how%20a%20100%20parameter%20FC%20NN%20looks%20like%20.svg)

As this is the building blocks of DNN's and AI's we can experiment with a lot of concepts with this, 

So collaborators are welcomed to clone this and try experimenting with this.

Install using PyPI (Python Package Index) 

```bash

py -m pip install --index-url https://test.pypi.org/simple/ --no-deps  QuantGrad

```