Build a Simple Neural Network to Read Handwritten Postal Codes
Overview
What this challenge is about.
Build two PyTorch models to read handwritten postal codes, then compare your CNN accuracy to a vendor benchmark. Earn a verifiable certificate.
The scenario
The provider (around 1,200 employees, around 900,000 parcels per week) currently pays its vendor about INR 0.40 per parcel for OCR; a viable in-house model would target under INR 0.10 fully loaded.
The Brief
What you'll do, and what you'll demonstrate.
Train and evaluate a small neural network for handwritten digit recognition that the operations team can compare to a vendor OCR module.
Earning criteria — what you'll demonstrate
- Implement a feed-forward and a convolutional neural network from scratch
- Use validation splits, early stopping, and regularization correctly
- Reason about per-class errors instead of relying only on top-1 accuracy
- Translate a model evaluation into a business comparison
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Neural Networks
Apply neural networks to solve real industry problems and demonstrate production-level capability.
- Convolutional Neural Networks
Apply convolutional neural networks to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Regularization
Apply regularization to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Implementing and comparing baseline neural networks in PyTorch with proper validation discipline is the bread-and-butter of an entry-level MLE role.
This challenge sharpens
- neural-networks
- pytorch
- regularization
Computer Vision Engineer
A CNN on a real OCR problem is the smallest possible computer-vision portfolio piece and a credible interview talking point for junior CV roles.
This challenge sharpens
- convolutional-neural-networks
- pytorch
- model-evaluation
AI Engineer
Comparing in-house vs. vendor with both accuracy and cost framing is exactly the kind of build-vs-buy memo AI engineers write in their first year.
This challenge sharpens
- python
- model-evaluation
- neural-networks