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- CodeIntermediateNew
Build a Neural Surrogate for Computational Fluid Dynamics in HVAC Design
Use a published CFD dataset (e.g., AirfRANS or a small in-house dataset if available) of around 1,000 steady-state airflow simulations on 2D building zones. Train a Fourier Neur…
- Neural Operators
- Surrogate Modeling
- Computational Fluid Dynamics
AI for Science and Engineering - ResearchIntermediateNew
Visual Question Answering for a Pediatric Radiology Workflow
You receive ~8,000 publicly available pediatric chest X-rays with structured findings labels (anonymized; no PHI access required). Build a VQA pipeline that maps a (image, quest…
- Vision Language Models
- Visual Question Answering
- Lora Finetuning
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 - CodeBeginnerNew
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
- Model Evaluation
Open coursework 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
- CodeSeniorNew
Train a GAN for Synthetic Defect Augmentation on a Factory Line
You receive a labeled defect dataset (12 defect types, ranging from 8 to 4,200 examples each), the production classifier, and a starter StyleGAN2-ADA codebase. Train a GAN per r…
- Gans
- Stylegan
- Data Augmentation
Open coursework - CodeIntermediateNew
Multi-View Pose Estimation for a Sports-Analytics Startup
Use the publicly-released SoccerNet or a synthetic 4-view dataset (you can render with Unity or use a provided one). Implement a 2D pose estimator per view (HRNet or YOLOv8-pose…
- Pose Estimation
- Multi View Geometry
- 3d Reconstruction
Open coursework - CodeIntermediateNew
Semantic Parser for an Enterprise Analytics Assistant
Define a small typed query language (filter, aggregate, group_by, time_range, metric). Curate or write 200 training examples covering the controlled subset and 50 held-out test …
- Semantic Parsing
- Grammar Design
- Transformer Models
Computational Semantics - 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
Open coursework - Browse challenges
Explore role
Strategy Analyst
Frame the business question, model the options, build the recommendation. From market sizing to competitive analysis, this role is where strategy consulting meets in-house decision-making.
- 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 - 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 - 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 - CodeIntermediateNew
Train an Object Detector for an Autonomous-Forklift Robotics Startup
You receive 12,000 labeled warehouse images (pallets, pedestrians, forklifts) plus a 1,500-image safety-test set heavy on pedestrian edge cases. Train an object detector (YOLOv8…
- Object Detection
- Yolo
- Edge Deployment
Deep Learning for Computer Vision Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
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
Open coursework - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework - ResearchSeniorNew
Validate a Foundation Model for Protein-Ligand Docking Acceleration
Pick 20 publicly available protein-ligand complexes from the PDBbind dataset (or similar public source). Use a published open-source structural foundation model (e.g., a Boltz-s…
- Foundation Model Evaluation
- Structural Biology
- Model Validation
Open coursework - CodeIntermediateNew
Fine-Tune a Sequence-to-Sequence Model for Code-Doc Generation
Take a small base model (CodeT5+ or a distilled CodeLlama-Instruct). Build the dataset by mining around 8,000 high-quality function-docstring pairs from permissively-licensed Py…
- Seq2seq
- Transformers
- Lora Fine Tuning
Neural Networks for NLP - ResearchIntermediateNew
Multi-Task Learning for a Healthtech Triage Model
You receive 40,000 anonymized de-identified intake-form records with two labels: urgency tier (4 classes) and routed sub-specialty (12 classes). Train (1) two independent classi…
- Multi Task Learning
- Transfer Learning
- Transformer
Open coursework - CodeIntermediateNew
Build a Domain-Specific Named-Entity Recognizer for Legal Contracts
Start from a strong English NER base (spaCy transformer or LegalBERT). Fine-tune on a provided 1,200-contract labeled dataset for the 9 entity types. Handle long contracts (ofte…
- Named Entity Recognition
- Sequence Labeling
- Domain Adaptation
Open coursework - 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 - 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
- Self Supervised Learning
Open coursework - ResearchSeniorNew
Diffusion-Policy Imitation for Bimanual Cooking Tasks
You receive 300 teleoperated demonstrations of a bimanual pour-and-stir task in a Robomimic-style simulator, deliberately including 2 valid solution modes per task (left-pour-ri…
- Diffusion Policies
- Imitation Learning
- Multimodal Action Distributions
Robot 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 - CodeSeniorNew
Run a Backpropagation Bug-Hunt on an Open-Source RL Implementation
You receive the offending fork (around 4,000 lines of PyTorch) and three known-failure seeds. Reproduce the NaN failure deterministically, instrument the forward and backward pa…
- Backpropagation
- Pytorch
- Debugging
Deep Learning - 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
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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