Ongoing Advanced Deep Learning Projects
Budget: ₹600 – ₹1,500 INR
Over the next year I’ll be running a series of computer-vision initiatives that require solid, production-ready Python code. The work spans the full pipeline: cleaning and augmenting large-scale image datasets, architecting and training deep networks, then stress-testing their performance before deployment. Knowledge in quantum computing is an added advantage
You’ll dive into convolutional and recurrent architectures for vision tasks—think hybrid CNN-RNN pipelines for sequence labelling—as well as exploratory projects where a GAN may generate synthetic images for data balancing and style transfer. Everything is image-based, so familiarity with libraries such as OpenCV for preprocessing and TensorFlow or PyTorch for model building is essential.
What I need from you on an ongoing basis:
• Robust preprocessing scripts that handle thousands of images with clear logging and reproducibility.
• Well-structured model code with modular components, hyper-parameter configs, and GPU optimisation.
• Evaluation reports that include accuracy metrics, visualisations, and concise recommendations for iteration.
All code should be documented and accompanied by a brief usage note or notebook. If you enjoy iterative experimentation, clean coding habits, and long-term collaboration, let’s get started.
Interested candidates please share samples/Github code.
PLEASE NOT THAT NO AI GENERATED CODE MUST BE USED
You’ll dive into convolutional and recurrent architectures for vision tasks—think hybrid CNN-RNN pipelines for sequence labelling—as well as exploratory projects where a GAN may generate synthetic images for data balancing and style transfer. Everything is image-based, so familiarity with libraries such as OpenCV for preprocessing and TensorFlow or PyTorch for model building is essential.
What I need from you on an ongoing basis:
• Robust preprocessing scripts that handle thousands of images with clear logging and reproducibility.
• Well-structured model code with modular components, hyper-parameter configs, and GPU optimisation.
• Evaluation reports that include accuracy metrics, visualisations, and concise recommendations for iteration.
All code should be documented and accompanied by a brief usage note or notebook. If you enjoy iterative experimentation, clean coding habits, and long-term collaboration, let’s get started.
Interested candidates please share samples/Github code.
PLEASE NOT THAT NO AI GENERATED CODE MUST BE USED