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.
- CodeIntermediateNew
Segment Cells from Microscopy Images for a Pharma-AI Discovery Lab
You receive 3,500 microscopy images with pixel-level cell masks plus a 200-image hold-out set re-annotated by two biologists for inter-annotator agreement. Train a U-Net or SegF…
- Semantic Segmentation
- U Net
- Pytorch
Open coursework - 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
- Graph Neural Networks
Advanced Machine Learning - 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
Open coursework - ResearchIntermediateNew
Prototype a Normalizing Flow for Anomaly Scoring in Climate Sensor Data
You receive 12 months of multivariate sensor traces (8 channels per sensor, hourly). Train a Normalizing Flow (Real NVP or a small Neural Spline Flow) on a clean training window…
- Normalizing Flows
- Density Estimation
- Anomaly Detection
Open coursework 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
- ResearchSeniorNew
Investigate Why Our Generative Model Memorizes Training Data
Pick a small open-source diffusion model (e.g., a Stable-Diffusion-class community model trained on LAION-subset). Reproduce a published membership-inference + extraction probe …
- Generative Models
- Memorization Analysis
- Differential Privacy
Advanced Deep 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
- Graph Neural Networks
- Scalable Ml
Open coursework - ResearchSeniorNew
Pretrain a Small Vision Transformer with Self-Supervised Learning
You receive 80,000 unlabeled 224x224 histology tiles plus 4,000 labeled tiles split into train/val/test. Pretrain a ViT-Small using a self-supervised method of your choice (DINO…
- Self Supervised Learning
- Vision Transformers
- Pytorch
Advanced Deep Learning - CodeIntermediateNew
Adapt Machine Translation to a Niche Domain
Pick an open MT base (NLLB-200 or a strong open M2M model). Build a parallel corpus of around 8,000 sentence pairs from the company's bilingual safety standards. Fine-tune on th…
- Machine Translation
- Domain Adaptation
- Transformers
Natural Language Processing - Browse challenges
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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.
- ResearchIntermediateNew
Train a Physics-Informed Neural Network for Heat Transfer in a Battery Pack
Solve the 2D unsteady heat-conduction equation on a square cell cross-section with a localized source and Dirichlet boundary conditions on the casing. Implement a baseline finit…
- Physics Informed Neural Networks
- Partial Differential Equations
- Pytorch
AI for Science and Engineering - ResearchIntermediateNew
Fine-Tune a Vision-Language Model for Image Captioning
Take BLIP-2 or LLaVA-1.6 as the base. Fine-tune (LoRA is fine) on a 4,000-image accessibility-curated dataset where each image has a useful caption written by a low-vision-exper…
- Vision Language Models
- Lora Fine Tuning
- Pytorch
Multimodal Machine Learning - 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 - ResearchSeniorNew
Compare RNN vs Transformer for Long-Sequence Modeling
Pick a public trajectory dataset (e.g., Argoverse 2, Waymo Open, or ETH-UCY). Implement three models with comparable parameter counts (around 5M each): an LSTM baseline, a vanil…
- Transformers
- Rnn
- State Space Models
Open coursework 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
- AnalysisIntermediateNew
Structured Prediction for Insurance Claim Triage
You receive 18,000 historical claims with text, attachments-count, claim amount, customer tenure, and the ground-truth final routing bucket. Train a structured classifier (e.g.,…
- Structured Prediction
- Multi Class Classification
- Model Evaluation
Advanced Machine Learning - 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
Open coursework - ResearchSeniorNew
Train Cooperative Agents with Multi-Agent RL
Pick an open multi-agent environment (PettingZoo's MPE 'simple_spread', Overcooked-AI, or SMAC). Implement or wrap three methods: IPPO (independent PPO per agent), MAPPO (centra…
- Multi Agent Reinforcement Learning
- Ppo
- Pytorch
Open coursework - CodeIntermediateNew
Triage Medical-Imaging Annotations with a Small Vision Model
Train a binary normal/abnormal classifier on the public CheXpert or NIH ChestX-ray14 dataset. Use temperature scaling to calibrate the output, then define abstention thresholds …
- Image Classification
- Transfer Learning
- Calibration
Applied Machine Learning - 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 - CodeBeginnerNew
Compare MDP Solvers for a Smart-Grid Battery Dispatch Pilot
Model home-battery dispatch as a finite MDP: state is (state-of-charge, hour-of-day, current price tier), actions are charge/hold/discharge with realistic efficiency losses, tra…
- Markov Decision Processes
- Value Iteration
- Policy Iteration
Artificial Intelligence: Principles and Techniques - 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
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
Open coursework - CodeBeginnerNew
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
Open coursework - CodeBeginnerNew
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
Open coursework - ResearchSeniorNew
Benchmark Long-Context Architectures on a Legal-Doc Retrieval Task
You receive a public legal-QA dataset (e.g., LongBench's legal split or similar) filtered to documents over 50,000 tokens. Implement or wrap 3 architectures: a sliding-window Tr…
- Long Context Architectures
- State Space Models
- Transformers
Advanced Deep Learning - CodeIntermediateNew
LoRA Fine-Tune a 7B LLM for Legal-Clause Extraction
You receive a curated extraction dataset (2,000 train, 500 val, 500 test contracts with span-level labels across 12 clause types) and a fine-tunable 7B base model (e.g., Llama-3…
- Lora
- Fine Tuning
- Parameter Efficient Tuning
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.
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