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Free Problems
🎶Learn AI via Pop Music - GAN(Generative Adversarial Networks)
This problem set explores the fundamental concepts of Generative Adversarial Networks (GANs) as presented in the educational song "GAN GAN GAN!". The problems cover the adversarial training process, different GAN architectures, loss functions, and key innovations in the field. Work through these problems to test your understanding of how GANs create synthetic data through the competition between generator and discriminator networks.
26 pts Medium 97 generative adversarial networks machine learning neural networks +7
🎶Learn AI via Pop Music - Diffusion Model
This problem set explores the key concepts of diffusion models as presented in the song. You'll learn about the mathematical foundations, practical implementations, and advanced techniques used in modern diffusion-based AI systems. The problems progress from basic concepts to advanced mathematical formulations, testing your understanding of noise scheduling, reverse processes, and optimization techniques mentioned in the lyrics.
32 pts Medium 99 diffusion models machine learning training process +7
🎶Learn AI via Pop Music - Let's Train The Model
This problem set covers key concepts in deep learning training from the song "Let's Train The Model". You'll explore initialization methods, activation functions, normalization techniques, optimization algorithms, and regularization strategies that are essential for training effective neural networks. The problems progress from basic concepts to advanced analytical thinking about model training dynamics.
25 pts Easy 98 machine learning gradient descent regularization +7
🎶Learn AI via Pop Music - Attention Is All You Need
This problem set explores the key concepts from the Transformer architecture as presented in the "Attention Is All You Need" video. The problems cover the fundamental components that make Transformers revolutionary in natural language processing, including attention mechanisms, encoder-decoder structure, positional encoding, and modern implementations like BERT and GPT. Work through these problems to master the architecture that powers modern AI systems.
29 pts Medium 99 transformer architecture attention mechanism neural networks +7
The Misconception that Almost Stopped AI - Gradient Descent and Loss Landscapes
This problem set explores the fundamental concepts of how AI models learn through gradient descent, based on the video "The Misconception that Almost Stopped AI [How Models Learn Part 1]". You'll work with concepts like loss functions, gradient descent, high-dimensional optimization, and the visualization challenges of neural network training landscapes. These problems progress from basic understanding to advanced analytical thinking about how modern AI models overcome early misconceptions about local minima.
28 pts Medium 102 loss functions machine learning cross entropy +7
Chapter 08 - RAG Application Evaluation
This problem set focuses on evaluating RAG (Retrieval-Augmented Generation) applications using benchmark datasets and the RAGAS framework. You'll explore key evaluation metrics, benchmark design principles, and practical implementation considerations for assessing RAG pipeline performance. These questions test your understanding of how to systematically evaluate different components of a RAG system, from retrieval tool selection to answer generation quality.
27 pts Medium 99 ragas retrieval augmented generation evaluation metrics +7
Premium Problems
Python I/O and Data Pipeline Assessment - Part 4
20 questions focused on PyTorch Dataset/DataLoader design: map/iterable datasets, transforms, custom collate/padding, worker seeding/sharding, num_workers/pin_memory/prefetch_factor, caching, memmap/shared memory, batching by size, profiling, and performance tuning.
10.00 60 pts Medium 98 torch.utils.data.dataset pytorch dataset +7
Chapter 02 - Numeric Python
This problem set covers key concepts from Chapter 2: Vectors, Matrices, and Multidimensional Arrays. The problems test understanding of NumPy array fundamentals, including array creation, indexing, slicing, operations, and vectorized computing. Each question is designed to reinforce the core concepts presented in the chapter.
5.00 26 pts Medium 97 numpy-arrays array-attributes shape +7
USAAIO 2025 R1P3 - Logistic Regression Implementation
This problem focuses on implementing logistic regression from scratch using the Titanic dataset. You will work through data pre-processing, mathematical derivations, and implement both gradient descent and Newton's method for logistic regression. The dataset contains passenger information from the Titanic, and your goal is to predict survival based on various features.
10.00 48 pts Easy 93 data-loading pandas data-exploration +7
USAAIO 2025 R1P2 - Basics of Neural Network - From Linear Regression to DNN Training
This problem is about the basics of neural network. Each part has its particular purpose to intentionally test you something. Do not attempt to find a shortcut to circumvent the rule. And all coding tasks shall run on CPUs, **not GPUs**.
10.00 36 pts Easy 96 learning-rate-scheduler pytorch optimization +12
USAAIO 2025 R1P1 - Fibonacci Matrix Form
Let us consider the following sequence: $$ F_n = F_{n-1} + F_{n-2},\ \forall\ n \ge 2. $$
8.00 27 pts Medium 96 fibonacci sequence linear algebra matrix form +7
IAIO 2024 Part 2 - Machine Learning Algorithms and Deep Learning
This problem covers the remaining categories of the 2024 International Artificial Intelligence Olympiad (IAIO), focusing on machine learning algorithms and deep learning. You'll work through practical implementations of k-means clustering, deep learning architectures, and advanced machine learning theory including kernel methods and the Perceptron algorithm. The problems cover: - K-means clustering algorithm implementation and convergence - Deep learning architectures (DALL-E, Transformers) - Perceptron algorithm and kernel methods - Mathematical proofs and theoretical analysis - Parameter counting and computational complexity
10.00 44 pts Hard 99 k-means clustering euclidean distance machine learning +7

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USA AI Olympiad

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Featured PDFs

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Cover of System Design Interview: An Insider's Guide Volume 2
System Design Interview: An Insider's Guide Volume 2
116 questions 348 pts
Cover of System Design Interview: An Insider's Guide
System Design Interview: An Insider's Guide
108 questions 317 pts
Cover of UNICALLI: A UNIFIED DIFFUSION FRAMEWORK FOR COLUMN-LEVEL GENERATION AND RECOGNITION OF CHINESE CALLIGRAPHY
UNICALLI: A UNIFIED DIFFUSION FRAMEWORK FOR COLUMN-LEVEL GENERATION AND RECOGNITION OF CHINESE CALLIGRAPHY
10 questions 38 pts
Cover of The Principles of Deep Learning Theory
The Principles of Deep Learning Theory
107 questions 418 pts

Featured Books

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Cover of Acing the System Design Interview
Acing the System Design Interview
153 questions 456 pts
Cover of Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
190 questions 543 pts
Cover of Hands-On Machine Learning with Scikit-Learn and PyTorch
Hands-On Machine Learning with Scikit-Learn and PyTorch
200 questions 554 pts
Cover of Deep Reinforcement Learning Hands-On - Third Edition
Deep Reinforcement Learning Hands-On - Third Edition
222 questions 720 pts

Featured Videos

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Cover of Flow-Matching vs Diffusion Models explained side by side
Flow-Matching vs Diffusion Models explained side by side
10 questions 29 pts
Cover of Attention in transformers, step-by-step | Deep Learning Chapter 6
Attention in transformers, step-by-step | Deep Learning Chapter 6
10 questions 30 pts
Cover of Knowledge Distillation: How LLMs train each other
Knowledge Distillation: How LLMs train each other
10 questions 27 pts
Cover of Diffusion Model
Diffusion Model
10 questions 32 pts

Popular Topics

machine learning 56 deep learning 40 neural networks 35 reinforcement learning 33 system-design 28 grade5 27 optimization 14 large language models 13 attention mechanisms 13 combinatorics 13 system-architecture 13 natural language processing 12 aime problems 12 Number Sense 12 scalability 11 beginner 10 number theory 10 performance 10 transformers 9 capacity-planning 9
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