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.
- ResearchSeniorNew
Embodied Visual Reasoning for a Warehouse Pick Assistant
Use an embodied simulator (Habitat 3.0 or Isaac Sim — pick one and justify) to render 300 cluttered-bin scenarios with a target item label. For each scenario, build two reasonin…
- Embodied Vision
- Vision Language Models
- Visual Reasoning
Visual Intelligence and Visual Reasoning - 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 - ResearchSeniorNew
Design a Distributed-Training Strategy for a Mid-Sized LLM
You will write a 5-page design memo that picks a parallelism strategy for fine-tuning a 13B model on 32 H100 GPUs, with a tokens-per-second estimate, a memory-per-GPU calculatio…
- Distributed Training
- Parallelism Strategies
- LLM Training
Machine Learning at Scale - 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 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
Finetune a Diffusion Model for Sustainable-Fashion Mockups
You receive 1,200 product photos with paired captions and the brand's style guide. Fine-tune a Stable-Diffusion-class base model with LoRA (Low-Rank Adaptation, a parameter-effi…
- Diffusion Models
- Lora Finetuning
- Pytorch Or Tensorflow
Advanced Deep Learning - ResearchSeniorNew
Trajectory Prediction Model for Urban Robotaxis
Use the Argoverse 2 motion-forecasting dataset (open access). Train an LSTM baseline + a transformer challenger (e.g., a small Wayformer or HiVT). Evaluate on minADE/minFDE (min…
- Trajectory Prediction
- Transformer Models
- Evaluation
AI for Autonomous Vehicles - 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…
- Fine Tuning
- Fine Tuning
- Parameter Efficient Tuning
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 - 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.
- 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 - ResearchIntermediateNew
Tune a PPO Policy for an Energy-Storage Trading Bot
You receive 18 months of 15-minute Nordic spot-price data, a battery dynamics model (capacity, round-trip efficiency, degradation curve), and a rule-based baseline that earns ab…
- Policy Gradients
- Ppo
- Reinforcement Learning
Deep Reinforcement Learning - CodeSeniorNew
Offline RL for Robot-Arm Skill Reuse
You receive 5,000 logged trajectories (state, action, reward, next-state) across 12 tasks, with 9 tasks for training and 3 held out. Train an offline RL algorithm (CQL or IQL re…
- Offline Rl
- Conservative Q Learning
- Skill Reuse
Robot Learning - CodeIntermediateNew
Domain-Adapt an NLP Pipeline from News to Customer-Support Tickets
You receive 30,000 anonymized customer-support tickets (PT-BR + ES) plus the news-trained NER and intent models. Apply continued pretraining of a multilingual encoder (e.g., XLM…
- Transfer Learning
- Domain Adaptation
- Continued Pretraining
Meta-Learning, Transfer Learning, and Multi-Task 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
- ResearchIntermediateNew
Run an Adversarial-Robustness Audit on a Face-Liveness Model for a Fintech
You receive a stand-in face-liveness model with the same backbone as the production model plus a labeled evaluation set of 2,000 frames. Apply three standard digital attacks (FG…
- Adversarial Robustness Research
- Face Liveness
- Pytorch Or Tensorflow
Deep Learning for Computer Vision - 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 - CodeBeginnerNew
Build a Robust Image Classifier for a Climate-Tech Satellite Startup
You receive a labeled dataset of about 25,000 Sentinel-2 patches (positive = illegal construction visible, negative = not). The dataset is split by region AND by season so you c…
- Data Augmentation
- Deep Learning
- Pytorch Or Tensorflow
Advanced Deep Learning - 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 - CodeIntermediateNew
Extractive QA on Clinical Trial Protocols
You receive 500 anonymized protocol PDFs (already OCR-ed to text) and 1,200 labeled question-answer pairs where each answer is an exact text span. Build an extractive QA system:…
- Extractive Qa
- Reading Comprehension
- Model Finetuning
Question Answering and Conversational Systems - 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 - CodeIntermediateNew
Train a Differentially Private Classifier on Medical Records
Use Opacus (PyTorch DP-SGD library). Train a tabular classifier (small MLP + gradient-boosted features) with DP-SGD at the agreed epsilon/delta. Run an accuracy-vs-privacy front…
- Differential Privacy
- Dp Sgd
- Opacus
Privacy-Preserving 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 - 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 Or Tensorflow
- Model Evaluation
AI/ML Practicum and Hands-on Lab - CodeIntermediateNew
Fine-Tune ASR for a Healthcare Voice-Note Startup
You receive about 40 hours of de-identified clinician voice notes paired with corrected transcripts plus a medical-terminology lexicon (about 8,000 drug + procedure terms). Fine…
- Asr
- Speech Recognition
- Domain Adaptation
Speech Recognition and Spoken Language Processing - 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 - CodeIntermediateNew
Build a Multilingual Customer-Email Classifier
You receive 28,000 labeled emails (skewed toward English and Mandarin). Try at least two approaches: (1) a fine-tuned multilingual transformer (XLM-RoBERTa or mDeBERTa) and (2) …
- Text Classification
- Multilingual NLP
- Hugging Face Transformers
Natural Language Processing
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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