PyTorch Or TensorFlow
If you like applying PyTorch Or TensorFlow, every challenge here gives you a chance to practice it on a real industry brief.
- CodeIntermediateNew
Build a Wake-Word Detector for a Smart-Speaker Startup
You receive a small public Japanese-speech dataset, 30 hours of recorded wake-phrase utterances from 50 volunteers, and 200 hours of background-noise recordings. Train a lightwe…
- Keyword Spotting
- Speech Recognition
- On Device Ml
Speech Recognition and Spoken Language Processing - CodeIntermediateNew
Ship a Churn-Prediction Mini-Project End to End
You receive a 12-month anonymized dataset of subscriber events (logins, lesson completions, payment history, support tickets) for around 200,000 users. Define churn precisely (n…
- Feature Engineering
- Model Evaluation
- Gradient Boosting
AI/ML Practicum and Hands-on Lab - CodeIntermediateNew
Quantize a Vision Model for a Smart-Doorbell SoC
You receive a trained FP32 PyTorch person-detector (mAP 0.74 on a 5k validation set) plus a calibration dataset of 500 unlabeled doorbell frames. Convert to ONNX, then apply pos…
- Quantization
- Model Optimization
- ONNX Optimization
Edge ML and On-Device Machine Learning - CodeIntermediateNew
Semantic Segmentation for a Solar-Panel Inspection Drone
Use a publicly-available solar-panel dataset (or the PV-Defect-Detection dataset). Fine-tune a small U-Net or SegFormer-tiny on panel/no-panel pixel-level segmentation. Evaluate…
- Semantic Segmentation
- CNN Classification
- Transfer Learning
Computer Vision (Undergraduate) Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Build a Crop-Disease Classifier for a Smallholder Agritech Startup
You receive a curated 22,000-image cassava-disease dataset across 5 classes (4 diseases + healthy) plus a labeled 1,200-image held-out test set. Train a CNN classifier (start wi…
- CNN Classification
- Cnn Architectures
- Transfer Learning
Deep Learning for Computer Vision - CodeIntermediateNew
Image-Quality Triage Tool for a Tele-Radiology Network
You receive 10,000 chest-X-ray images with multi-label quality flags (rotation, clipping, motion). Train a small multi-label CNN that outputs a per-flag probability and a single…
- Medical Imaging
- Classification
- Neural Networks
Machine Learning for Imaging and Medical Image Analysis - CodeIntermediateNew
Team Practicum: Build a Crop-Disease Classifier with a Field Partner
You receive a labeled dataset of about 8,000 phone photos plus around 1,200 unlabeled photos from a held-out county. Audit and clean the labels (expect 5-10% noise), train a Mob…
- Transfer Learning
- PyTorch Or TensorFlow
- Model Evaluation
AI/ML Practicum and Hands-on Lab - CodeIntermediateNew
Reduce Dimensionality on Sensor Streams for a Mid-Cap Robotics OEM
You receive 120 robot-hours of windowed sensor data (5s windows, 240 channels) with labels for normal vs. one of four fault classes. Implement (1) PCA, (2) kernel PCA with an RB…
- Dimensionality Reduction
- Kernel Methods
- Autoencoders
Machine Learning - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeIntermediateNew
Build an MLP Baseline for Credit-Default Risk at a Fintech
You receive 18 months of anonymized credit-decision data (around 600,000 applications, 80 features) with a 90-day default label. Train an MLP with regularization (dropout, weigh…
- Mlp
- Regularization
- Tabular Deep Learning
Deep Learning - CodeIntermediateNew
Build a Robust Image Classifier for a Climate-Tech Satellite Startup
You receive a labeled dataset of about 25,000 Sentinel-2 patches (positive = illegal construction visible, negative = not). The dataset is split by region AND by season so you c…
- Data Augmentation
- Deep Learning
- PyTorch Or TensorFlow
Advanced Deep Learning - CodeIntermediateNew
End-to-End Lane Following on a Donkeycar Platform
Use the public Donkeycar Tub dataset (or collect about 30 minutes of driving on the simulator). Train a CNN-policy baseline (the Donkeycar default architecture is fine) that pre…
- End To End Learning
- Imitation Learning
- PyTorch Or TensorFlow
AI for Autonomous Vehicles - DesignIntermediateNew
Privacy-Preserving Crowd-Density Estimator for Transit Stations
Use a public crowd-counting dataset (e.g., ShanghaiTech or JHU-CROWD) to train a small crowd-density estimator (CSRNet or similar). Wrap it in an on-device pipeline (Python is f…
- Crowd Counting
- Scene Understanding
- Privacy By Design
Visual Intelligence and Visual Reasoning Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Behavior Cloning for a Pick-and-Place Manipulator
You receive 200 human teleoperated demonstrations (state + action trajectories) of picking 8 small electronic components from a tray and placing them at marked locations in a ro…
- Behavior Cloning
- Imitation Learning
- Manipulation
Robot Learning - CodeIntermediateNew
Build a Video-Question-Answering Demo on a Budget
Pick the model (Video-LLaVA, VideoChat2, or LLaVA-Video) and justify on the A10G budget. Build a Streamlit demo: upload video, ask question, get answer with cited frame timestam…
- Video Language Models
- Multimodal Fusion
- Streamlit
Multimodal Machine Learning
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.