Sudoku Solver using CNN

[ashis2004]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title :
Sudoku Solver using CNN
🔴 Aim :

Network was able to solve the puzzles with 95% accuracy.

🔴 Dataset : (https://www.kaggle.com/bryanpark/sudoku) for this project.

🔴 Approach :

File	Description
sudoku.ipynb	notebook for running and testing the project
model.py	neural network implementation
report.pdf	Step by step implementataion and description of project

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

---

🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

---

✅ To be Mentioned while taking the issue :

• Full name : Ashish Kumar Patel
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? (Mention the Open Source program): GSsoc'24 contributor

---

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[ashis2004]

@abhisheks008 pls assign it me I want to work on it

[abhisheks008]

Apart from CNN, what are the other models you can implement for this problem statement? To be a contributor in this repository you need to implement at least 3 models and find out the best fitted model by comparing the implemented models based on their accuracy scores.

[ashis2004]

Variational Autoencoder (VAE): VAE to generate and solve Sudoku puzzles.

Recurrent Neural Networks (RNN): RNNs or LSTMs for sequence learning.

Generative Adversarial Networks (GANs): GANs for generating and solving Sudoku puzzles.

@abhisheks008 pls assign it to me next time I'll keep in mind

[abhisheks008]

Variational Autoencoder (VAE): VAE to generate and solve Sudoku puzzles.

Recurrent Neural Networks (RNN): RNNs or LSTMs for sequence learning.

Generative Adversarial Networks (GANs): GANs for generating and solving Sudoku puzzles.

@abhisheks008 pls assign it to me next time I'll keep in mind

Cool @ashis2004 you can go ahead. Follow the project structure and put arrange all the files as per the structure.

[abhisheks008]

Variational Autoencoder (VAE): VAE to generate and solve Sudoku puzzles.
Recurrent Neural Networks (RNN): RNNs or LSTMs for sequence learning.
Generative Adversarial Networks (GANs): GANs for generating and solving Sudoku puzzles.
@abhisheks008 pls assign it to me next time I'll keep in mind

Cool @ashis2004 you can go ahead. Follow the project structure and put arrange all the files as per the structure.

Hi @ashis2004 finish the assigned issue first then only you can start working on this issue.

[Sanjeev-Kumar78]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title: Sudoku Solver using CNN

🔴 Aim: To develop a deep learning model using Convolutional Neural Networks (CNN) to solve Sudoku puzzles automatically.

🔴 Dataset: Kaggle.

🔴 Approach: Implement and compare at least three models to find the best-fitted algorithm by checking the accuracy scores. Also, perform exploratory data analysis before creating any model.

Models to be implemented:

1. Baseline CNN: A simple CNN model to set a baseline.
2. Improved CNN: A deeper and more complex CNN architecture.
3. Hybrid Model (CNN + RNN): Combines CNN for feature extraction and RNN for sequence prediction.

---

📍 Follow the Guidelines to Contribute to the Project:

• Create a separate folder named as the Project Title.
• Inside that folder, there will be four main components:
  • Images - To store the required images.
  • Dataset - To store the dataset or information/source about the dataset.
  • Model - To store the machine learning models created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project on other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

---

🔴🟡 Points to Note:

• The issues will be assigned on a first-come, first-served basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include the issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before starting to contribute.

---

✅ To be Mentioned while taking the issue:

• Full name: Sanjeev Kumar
• GitHub Profile Link: https://www.github.com/Sanjeev-Kumar78
• Email ID:[email protected]
• Participant ID (if applicable):
• Approach for this Project:
• What is your participant role? (Mention the Open Source program)
  GSSOC'24 Contributor role

---

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[abhisheks008]

Hi @Sanjeev-Kumar78 this issue is opened by another contributor, hence it can't be assigned to you.

[Sanjeev-Kumar78]

Got it, no problem.