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- ResearchIntermediateNew
Red-Team an Image-Classification Pipeline for a Banking KYC Workflow
You receive the production image classifier as a black-box API plus a labeled validation set of 5,000 ID images. Run untargeted FGSM and PGD attacks (L_inf budget 4/255 and 8/25…
- Adversarial Attacks
- Robust Evaluation
- Red Teaming
Trustworthy AI, Robustness, and Safety - ResearchIntermediateNew
Probe a Pretrained Encoder for Linguistic Knowledge
Take BERT-base (or DeBERTa-v3-base). Run layer-wise probes across at least 3 linguistic tasks: part-of-speech tagging, dependency arc classification, and semantic role labeling.…
- Interpretability
- Probing
- Transformers
Neural Networks for NLP - ResearchSeniorNew
Train a Small Diffusion Model for Synthetic Defect Generation
You receive 2,000 labeled defect images and 18,000 clean weld images. Train a small class-conditional latent diffusion model on the defect images (Hugging Face diffusers is fine…
- Generative Perception
- Diffusion Models
- Data Augmentation
Machine Perception - 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 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
- 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 - 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
Neural Networks for NLP - 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
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
Neural Networks for NLP - Browse challenges
Explore role
Pricing Strategist
Set the price that captures value without leaving sales on the table. Demand modelling, willingness-to-pay research, and the disciplined experimentation that turns pricing into a competitive advantage.
- CodeIntermediateNew
Actor-Critic for Energy-Storage Dispatch
You receive 3 years of hourly day-ahead price data and a Python simulator that models state of charge, round-trip efficiency, and a 1-day price forecast with documented uncertai…
- Actor Critic
- A2c
- Deep Rl
Reinforcement 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 - ResearchIntermediateNew
Lab Project: Compare Three Architectures on Your Own Mini-Benchmark
Scope the problem yourself (suggested examples: sentiment classification on a niche domain, tabular anomaly detection, time-series forecasting on a public dataset). Define the t…
- Experiment Design
- Statistical Testing
- Pytorch
AI/ML Practicum and Hands-on Lab - 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) Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
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
Advanced Deep Learning - CodeBeginnerNew
Build an MLP Baseline for Credit-Default Risk at a Fintech
You receive 18 months of anonymized credit-decision data (around 600,000 applications, 80 features) with a 90-day default label. Train an MLP with regularization (dropout, weigh…
- Mlp
- Regularization
- Tabular Deep Learning
Deep Learning - 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
Multi-Agent Systems - CodeBeginnerNew
Quantize a Vision Model for a Smart-Doorbell SoC
You receive a trained FP32 PyTorch person-detector (mAP 0.74 on a 5k validation set) plus a calibration dataset of 500 unlabeled doorbell frames. Convert to ONNX, then apply pos…
- Quantization
- Model Optimization
- Onnx
Edge ML and On-Device Machine Learning - 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
Speech Recognition and Spoken Language Processing - 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 - AnalysisIntermediateNew
Compare Stereo Depth Methods for a Drone Inspection Startup
You receive 500 calibrated stereo pairs from a turbine inspection plus sparse LiDAR ground truth on each pair. Implement (or wrap) three depth estimators: OpenCV Semi-Global Mat…
- Stereo Depth Estimation
- Multi View Geometry
- Model Evaluation
3D Vision and Multi-View Geometry - CodeIntermediateNew
Parse and Structure Clinical Discharge Summaries
Combine traditional NLP (section segmentation, sentence parsing) with LLM extraction (small open model + structured-output enforcement). Build the pipeline so every extracted fi…
- Structured Extraction
- Clinical NLP
- Parsing
Natural Language Processing - 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
Deep Reinforcement Learning - 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
Visual Intelligence and Visual Reasoning - 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
Generative AI - ResearchSeniorNew
Certify Robustness for a Medical-Imaging Classifier
You receive the classifier (a PyTorch ResNet variant) and a 4,000-image labeled validation slice. Apply randomized smoothing (Cohen et al.) at sigma in {0.25, 0.5, 1.0}. Report …
- Certified Robustness
- Randomized Smoothing
- Formal Verification
Trustworthy AI, Robustness, and Safety
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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