AI & Data
Deep Learning Challenges
Deep Learning challenges put you inside the work of building models that learn from raw data. You'll develop skills in Neural Networks and Feedforward Networks, apply Data Augmentation, and train models in PyTorch or TensorFlow alongside Reinforcement Learning fundamentals.
From there you'll handle the harder edges — Transformer architecture, Attention mechanisms, Custom architecture design, and Distributed training — working with PyTorch Lightning / Hugging Face Trainer, JAX research patterns, and Ablation study design the way research teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
· Neural Networks Clear- ResearchSeniorNew
Graph Transformer Research Probe for a Drug-Target Predictor
You receive a public drug-target interaction dataset (around 50,000 drug-target pairs with labels and molecular graphs), a strong GIN baseline, and a starter GraphGPS implementa…
- Graph Transformers
- Neural Networks
- Message Passing
Machine Learning on Graphs - CodeSeniorNew
Triage Brain-CT Stroke Detector with Calibrated Uncertainty
You receive a curated public head-CT dataset (about 2,800 scans, slice-level labels for hemorrhagic stroke) and a held-out 600-scan hospital cohort. Train a 3D CNN or 2.5D slice…
- Medical Imaging
- Neural Networks
- Uncertainty Quantification
Machine Learning for Imaging and Medical Image Analysis - AnalysisIntermediateNew
Transfer-Learning Backbone Bake-Off for Retail Product Tagging
You receive 80,000 retail product images tagged with multiple labels from a 250-tag taxonomy. Use each of the three pretrained backbones via two transfer strategies: (1) linear …
- Transfer Learning
- Fine Tuning
- Supervised Learning
Meta-Learning, Transfer Learning, and Multi-Task Learning - ResearchIntermediateNew
Benchmark Graph-Embedding Methods on a Climate-Network Dataset
You receive a 200M-edge sample of the knowledge graph and a labeled entity-similarity test set (5,000 pairs with relevance labels). Benchmark three methods: a shallow embedding …
- Graph Embeddings
- Neural Networks
- Scalable Ml
Machine Learning at Scale 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
- ResearchSeniorNew
Train a Small Diffusion Model for Synthetic Defect Generation
You receive 2,000 labeled defect images and 18,000 clean weld images. Train a small class-conditional latent diffusion model on the defect images (Hugging Face diffusers is fine…
- Generative Perception
- Diffusion Models
- Data Augmentation
Machine Perception - CodeIntermediateNew
Train a GNN for Fraud-Ring Detection at a Payments Fintech
You receive an anonymized transaction dataset (around 120,000 merchants, around 4 million transactions over 12 months, around 2% labeled fraud) and the team's LightGBM baseline.…
- Neural Networks
- Graphsage
- Fraud Detection
Machine Learning on Graphs - ResearchSeniorNew
Pre-Register and Run a Small Neural-Network Ablation Study
You will study how three architectural and regularization choices (depth: 2/4/8 hidden layers; activation: ReLU vs. GELU; weight decay: 0 / 1e-4 / 1e-3) affect a small MLP's tes…
- Neural Networks
- Regularization
- Experimental Design
Machine Learning - CodeBeginnerNew
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 - 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.
- AnalysisSeniorNew
Brain-Tumor MRI Segmentation Bake-Off
You receive a curated public multi-modal MRI brain-tumor cohort (~600 patients, T1/T1c/T2/FLAIR with whole-tumor / tumor-core / enhancing-tumor masks). Train all three architect…
- Medical Imaging
- Segmentation
- Neural Networks
Machine Learning for Imaging and Medical Image Analysis - CodeSeniorNew
Fuse Camera + Audio Cues for an Autonomous-Vehicle Edge Case
You receive a curated dataset of 4,000 short clips (5s each), each with synchronized 8-camera 360-degree video, 4-channel audio, and labels (siren-active emergency vehicle prese…
- Multimodal Perception
- Neural Networks
- Audio Processing
Machine Perception - ResearchIntermediateNew
Kernel Methods vs. Deep Learning on a Tiny-Data Drug-Discovery Task
You receive (or download) 3 public ADMET datasets from MoleculeNet (e.g., BBBP, Lipophilicity, FreeSolv). For each, train both: (a) a Gaussian process with a Tanimoto kernel ove…
- Kernel Methods
- Gaussian Processes
- Neural Networks
Advanced Machine Learning - CodeFoundationalNew
Build a Simple Neural Network to Read Handwritten Postal Codes
You receive a labeled dataset of about 60,000 handwritten digit images (28x28 grayscale) drawn from Indian postal forms. Build two models from scratch in PyTorch: (1) a 2-layer …
- Neural Networks
- Neural Networks
- Pytorch Or Tensorflow
Machine Learning (Undergraduate) 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
- ResearchSeniorNew
Self-Supervised Pretraining for a Pathology Foundation Vendor
You receive a public pathology dataset (about 80,000 unlabeled whole-slide-image patches plus a labeled 8,000-patch subtype-classification subset across 4 classes). Pretrain a R…
- Supervised Learning
- Medical Imaging
- Transfer Learning
Machine Learning for Imaging and Medical Image Analysis - CodeIntermediateNew
Few-Shot Defect Classifier for a Fast-Onboarding Industrial AI Vendor
You receive a multi-customer defect dataset (8 historical customers, 4-6 defect classes each). Treat 6 customers as the meta-training set and 2 as the held-out 'new customer' sc…
- Meta Learning
- Few Shot Learning
- Prototypical Networks
Meta-Learning, Transfer Learning, and Multi-Task 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.