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
· PyTorch or TensorFlow Clear- DesignSeniorNew
Design a Distributed Training Job for a 13B-Parameter Model
Decide whether to use Fully Sharded Data Parallel (FSDP), Tensor Parallelism, Pipeline Parallelism, or a hybrid; justify against the 13B-param + 32-H100 setup. Calculate memory …
- Distributed Training
- Fsdp
- Pytorch
Open coursework - ResearchIntermediateNew
Build Saliency-Map Explanations for Dermatology Triage
You receive a trained CNN (ResNet-50 backbone, 7-class lesion classifier) and a 1,000-image held-out test set with dermatologist labels. Implement Integrated Gradients, GradCAM,…
- Saliency Maps
- Integrated Gradients
- Gradcam
Explainable and Interpretable AI - CodeIntermediateNew
Prune and Distill a Speech Model for a Hearable
You receive a trained 280 KB CNN keyword spotter (10 keywords + silence + unknown) with 96.1% top-1 accuracy on the Google Speech Commands test set. Apply structured pruning (ch…
- Pruning
- Knowledge Distillation
- Model Compression
Edge ML and On-Device Machine Learning - ResearchSeniorNew
Open-Vocabulary Segmentation Benchmark for a Robotics R&D Lab
Use a curated 200-image household scene set (publicly-available HM3D renderings or COCO + a handful of household prompts). Benchmark 3 open-vocabulary segmentation models: SAM +…
- Open Vocabulary Segmentation
- Vision Language Models
- Benchmarking
Computer Vision Develop in-demand professional skills.
Each challenge names the skills it strengthens. Over time, your profile fills with the competences a hiring manager would actually look for.
Why Ewance
- CodeBeginnerNew
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
Open coursework
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.