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- 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
- Experiment Design
Machine Learning - 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
- Convolutional Neural Networks
- Audio Processing
Machine Perception - CodeIntermediateNew
Train a Reward Model on Customer-Support Preferences
You receive 8,000 labeled preference pairs from real support conversations (each pair is two model responses with a human-chosen winner). Fine-tune a small open-weights base mod…
- Reward Modeling
- Preference Learning
- Bradley Terry Loss
Machine Learning from Human Preferences (RLHF and Alignment) - CodeIntermediateNew
Fine-Tune a Diffusion Model for an E-commerce Product Studio
You receive 1,200 curated product + lifestyle images across 6 product categories, a brand-style guide, and the company's current studio cost per image (around EUR 18). Fine-tune…
- Diffusion Models
- Stable Diffusion
- Dreambooth
Generative AI Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Train a Deep Q-Network for Warehouse Robot Routing
You receive a Gymnasium-compatible warehouse simulator (50x50 grid, 8 dynamic obstacle pedestrians, 20 randomized pick locations) and a baseline A* planner script. Train a DQN a…
- Deep Q Learning
- Reinforcement Learning
- Pytorch
Deep Reinforcement Learning - DesignBeginnerNew
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 - CodeSeniorNew
Video Action Recognition for a Retail Loss-Prevention Startup
Use a public action-recognition dataset (UCF101 + a small curated retail-action subset; the latter is provided synthetic or you can label 50 short clips). Fine-tune a small back…
- Video Understanding
- Action Recognition
- Transfer Learning
Computer Vision - CodeIntermediateNew
Generate Synthetic Tabular Data with Privacy Guarantees
Implement DP synthetic data generation: either DP-CTGAN, PATE-GAN, or a marginal-based DP method like PrivBayes / MWEM. Train on the real dataset (around 200,000 transactions, 1…
- Synthetic Data
- Differential Privacy
- Generative Models
Privacy-Preserving Machine Learning - Browse challenges
Explore role
Marketing Analyst
Plan and measure campaigns that grow the business. Funnel analytics, attribution, segmentation, and the rigorous measurement that lets marketing defend its budget at the leadership table.
- CodeIntermediateNew
Defect Detection on PCBs for a Hardware-AI Manufacturer
Use the publicly-available PCB defect dataset (e.g., DeepPCB or HRIPCB). Fine-tune a small object detector (YOLOv8n or RT-DETR-small) on the 6 defect classes. Evaluate mean Aver…
- Object Detection
- Transfer Learning
- Model Evaluation
Computer Vision - ResearchIntermediateNew
QLoRA Fine-Tune for a Customer-Support Domain Assistant
You receive 8,000 anonymized support ticket pairs (question -> agent response), the company's product documentation (around 600 pages), and a strong RAG baseline already running…
- Qlora
- Fine Tuning
- RAG
Fine-Tuning Large Language Models - CodeSeniorNew
Train a Manipulation Policy for Bin Picking with Imitation Learning
You receive a dataset of 500 teleop trajectories on the in-distribution part plus a held-out simulation environment with a never-seen part. Train an imitation-learning policy (D…
- Imitation Learning
- Manipulation
- Diffusion Policy
Advanced Robotics - CodeBeginnerNew
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 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…
- Self 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 - ResearchSeniorNew
Curriculum RL for a Simulated Drone Inspection Task
You receive a PyBullet-based wind-turbine inspection simulator with parameterizable wind, blade orientation, and sensor noise. Design a 3-stage curriculum: (1) hover near a stat…
- Ppo
- Curriculum Learning
- Deep Rl
Reinforcement Learning - CodeIntermediateNew
Build a Speaker-Diarization Pipeline for a Legal-Tech Startup
You receive 20 hours of de-identified hearing audio with ground-truth speaker labels (4 speaker classes per hearing). Build a speaker-diarization pipeline (pyannote-audio or sim…
- Speaker Diarization
- Speech Recognition
- Pyannote
Speech Recognition and Spoken Language Processing - CodeSeniorNew
Train a 3D Object Detector for Highway Trucking
Use the nuScenes or Waymo Open Dataset (open access) as your training and evaluation source. Fine-tune a strong baseline (e.g., CenterPoint or BEVFusion) and define an evaluatio…
- 3d Object Detection
- Perception
- Pytorch
AI for Autonomous Vehicles - 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
- Graph Neural Networks
- Scalable Ml
Machine Learning at Scale - CodeIntermediateNew
Variational Autoencoder for Synthetic Tabular Banking Data
You receive a 500K-row anonymized transaction dataset with 25 columns (mixed numerical + categorical). Train a VAE (TabVAE or a small custom model) with appropriate likelihoods …
- Variational Inference
- Deep Generative Models
- Synthetic Data
Probabilistic Machine Learning - CodeIntermediateNew
Quantize a CNN for Battery-Powered Wildlife Cameras at a Climate Nonprofit
You receive an FP32 CNN (MobileNetV2 fine-tuned to 22 species, around 13 MB) and a hold-out test set of 4,000 images. Quantize to int8 (post-training quantization first, then qu…
- Quantization
- Qat
- Edge Deployment
Deep Learning - 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
- Convolutional Neural Networks
Machine Learning for Imaging and Medical Image Analysis - CodeSeniorNew
Auto-Tune a Distributed Training Cluster's Throughput
Pick a representative fine-tune job (an open 7B model on a public instruction dataset is fine). Define the search space: NCCL_ALGO, NCCL_PROTO, num_workers, prefetch_factor, gra…
- Distributed Training
- Hyperparameter Tuning
- Nccl
Machine Learning Systems - ResearchSeniorNew
Audit a Production Model for Membership Inference Attacks
Use a black-box membership inference attack (e.g., the LiRA or shadow-model attack). You have query access to a sandboxed copy of the model + the original training data labels f…
- Membership Inference
- Privacy Attacks
- Model Evaluation
Privacy-Preserving Machine Learning - CodeIntermediateNew
Build an Audio-Visual Speaker Diarization Pipeline
Build the pipeline: face detection + active-speaker detection on video, voice-activity detection + speaker embeddings on audio, then a fusion step that ties tracks to detected f…
- Audio Visual Fusion
- Speaker Diarization
- Active Speaker Detection
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.