Conversion of Python AI to CiRA CORE Need it to be done by tomorrow
Budget: ₹600 – ₹1,500 INR
Description:
I have an existing AI project written in Python that includes model training, evaluation, and prediction functionality. The code is currently implemented using standard Python libraries (TensorFlow/PyTorch). I need this functionality recreated inside CiRA CORE (a no-code deep learning platform).
Your Main Tasks:
Understand the Current Python Project
Analyze existing code structure (data preprocessing, model architecture, training loop, evaluation).
Understand model requirements (e.g., IMDB sentiment analysis, image classification).
Recreate the Project in CiRA CORE
Set up datasets in CiRA CORE (e.g., text or image dataset import).
Build equivalent model architecture using CiRA CORE blocks (e.g., input layer, embedding, dense layers, activation, dropout, etc.).
Configure training parameters (optimizer, loss function, epochs, batch size).
Implement evaluation and prediction inside CiRA CORE.
Document the steps clearly (screenshots and explanation).
Validate
Ensure accuracy and performance are comparable to the original Python implementation.
Test with a sample dataset and provide results.
Deliverables:
A CiRA CORE project file (with all model layers and settings).
A short guide or video walkthrough explaining:
How to load the dataset.
How to train and evaluate the model.
How to use the trained model for predictions.
Requirements:
Experience with CiRA CORE or similar AI no-code tools.
Understanding of machine learning concepts and model conversion.
Ability to read and interpret Python AI code.
Estimated Project Size & Complexity
Existing Python project: Medium complexity (text classification or similar).
CiRA CORE recreation: Medium effort, mostly drag-and-drop with parameter configuration.
I have an existing AI project written in Python that includes model training, evaluation, and prediction functionality. The code is currently implemented using standard Python libraries (TensorFlow/PyTorch). I need this functionality recreated inside CiRA CORE (a no-code deep learning platform).
Your Main Tasks:
Understand the Current Python Project
Analyze existing code structure (data preprocessing, model architecture, training loop, evaluation).
Understand model requirements (e.g., IMDB sentiment analysis, image classification).
Recreate the Project in CiRA CORE
Set up datasets in CiRA CORE (e.g., text or image dataset import).
Build equivalent model architecture using CiRA CORE blocks (e.g., input layer, embedding, dense layers, activation, dropout, etc.).
Configure training parameters (optimizer, loss function, epochs, batch size).
Implement evaluation and prediction inside CiRA CORE.
Document the steps clearly (screenshots and explanation).
Validate
Ensure accuracy and performance are comparable to the original Python implementation.
Test with a sample dataset and provide results.
Deliverables:
A CiRA CORE project file (with all model layers and settings).
A short guide or video walkthrough explaining:
How to load the dataset.
How to train and evaluate the model.
How to use the trained model for predictions.
Requirements:
Experience with CiRA CORE or similar AI no-code tools.
Understanding of machine learning concepts and model conversion.
Ability to read and interpret Python AI code.
Estimated Project Size & Complexity
Existing Python project: Medium complexity (text classification or similar).
CiRA CORE recreation: Medium effort, mostly drag-and-drop with parameter configuration.