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- 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 - 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
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 - 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 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
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 - 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 - 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
Open coursework - CodeIntermediateNew
Build a GAN-Based Defect Generator for a Hardware Manufacturing Line
You receive around 60,000 good-unit images and around 380 defective-unit images across 4 defect classes. Train a class-conditional GAN (StyleGAN2-ADA or a smaller alternative fo…
- Gans
- Class Conditional Generation
- Data Augmentation
Deep Generative Models - 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.
- ResearchSeniorNew
Model-Based RL for a Robotic Arm Pick-Place Task
You receive a PyBullet pick-and-place environment (Franka Panda arm, 12 object types, randomized starting poses) and a SAC baseline that hits 85% success after about 1.5 million…
- Model Based Rl
- World Models
- Reinforcement Learning
Open coursework - CodeBeginnerNew
Reduce Dimensionality on Sensor Streams for a Mid-Cap Robotics OEM
You receive 120 robot-hours of windowed sensor data (5s windows, 240 channels) with labels for normal vs. one of four fault classes. Implement (1) PCA, (2) kernel PCA with an RB…
- Dimensionality Reduction
- Kernel Methods
- Autoencoders
Machine Learning - CodeIntermediateNew
Fine-Tune a Transformer for Customer-Support Triage at an Enterprise AI Vendor
You receive 240,000 labeled support tickets across 14 queues, with English, Bahasa Indonesia, and Tagalog. Fine-tune a multilingual transformer encoder (XLM-RoBERTa-base is a st…
- Transformers
- Fine Tuning
- Multilingual NLP
Deep 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 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
- CodeIntermediateNew
Train a Multimodal Classifier for Medical Triage
Pick a fusion architecture (early fusion via cross-attention, late fusion via score combination, or a unified multimodal encoder like FLAVA/CoCa). Train on the 14,000 pairs with…
- Multimodal Fusion
- Cross Attention
- Pytorch
Open coursework - CodeIntermediateNew
Build a Vision-Language Search for an E-commerce Catalog
Pick a vision-language encoder (OpenCLIP, SigLIP, or BLIP-2 image-text variant). Index all 600k product images into a vector database (Qdrant/FAISS). Build a query-time pipeline…
- Vision Language Models
- Clip
- Vector Search
Open coursework - ResearchIntermediateNew
Train a NeRF for Real-Estate Virtual Tours
You receive a curated dataset of 3 apartments, each with around 120 input images and known camera poses (already SfM-processed). Train a NeRF variant (Instant-NGP or Nerfacto re…
- Neural Scene Representation
- Nerf
- Pytorch
Open coursework - CodeIntermediateNew
Scene-Graph Generation for Retail Shelf Audits
You receive 1,500 labeled shelf photos (anonymized product crops, bounding boxes, and ~12 relation types). Build a pipeline that, for a new shelf photo, outputs (a) detected pro…
- Scene Graph Generation
- Object Detection
- Relation Prediction
Open coursework - 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 - 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
- Transformers
Open coursework - ResearchIntermediateNew
Train a Reinforcement-Learning Locomotion Policy for a Quadruped
You receive a configured Isaac Lab environment for the quadruped, a baseline PPO trainer, and a set of 8 trip-hazard / slip stress scenarios. Train the policy for a budget of ab…
- Reinforcement Learning
- Locomotion
- Domain Randomization
Open coursework - CodeIntermediateNew
DPO Fine-Tune for a Domain-Specific Writing Assistant
You receive a base instruction-tuned model checkpoint plus 2,500 preference pairs from editorial reviews (each pair: two grant-application paragraphs, the editor-preferred winne…
- Dpo
- Preference Learning
- Model Finetuning
Machine Learning from Human Preferences (RLHF and Alignment) - 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 - ResearchSeniorNew
Price American Options with a Deep Hedging Notebook
Simulate price paths for a single underlying (geometric Brownian motion is fine as a baseline; bonus for stochastic volatility). Implement Longstaff-Schwartz Monte Carlo as the …
- Deep Learning
- Stochastic Modeling
- Derivatives Pricing
AI and Quantitative Finance - 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 - CodeIntermediateNew
Build a Cross-Lingual Retrieval-Augmented QA System
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pip…
- RAG
- Cross Lingual Retrieval
- Multilingual Embeddings
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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