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Free Problems
Deep Q-Learning Fundamentals
This problem set covers key concepts from Chapter 2 on Deep Q-Learning, including decision problem classification, dynamic programming, Q-learning fundamentals, and practical implementation of DQL agents. The problems progress from basic conceptual understanding to advanced analytical thinking about reinforcement learning algorithms and their applications in finance and economics.
30 pts Medium 100 reinforcement learning decision making markov processes +7
Chapter 01 - Learning Through Interaction
This problem set covers key concepts from Chapter 1: Learning Through Interaction, focusing on Bayesian learning, reinforcement learning fundamentals, and the building blocks of RL algorithms. The problems progress from basic probability concepts to advanced RL principles, testing your understanding of how agents learn through interaction with environments.
25 pts Easy 96 probability expected value decision making +7
How AI Taught Itself to See [DINOv3]
This problem set explores self-supervised learning in computer vision, focusing on the DINO (self-DIstillation with NO labels) framework and its evolution to DINOv3. These methods enable AI models to learn meaningful visual representations without human-labeled data by creating their own supervision signals through image augmentations and knowledge distillation between student and teacher networks. The problems cover feature representation, contrastive learning, knowledge distillation, and the specific innovations that make DINOv3 effective for visual understanding tasks.
38 pts Medium 100 feature representation linear classifier data transformation +7
Advancing Diffusion Models for Text Generation
This problem set explores key concepts from Kilian Q. Weinberger's talk on advancing diffusion models for text generation. The problems cover knowledge separation in language models, latent diffusion for text, and controlling language models through diffusion processes. Work through these problems to understand the cutting-edge techniques discussed in the video.
46 pts Medium 100 large language models knowledge storage text generation +7
Text Diffusion - A New Paradigm for LLMs
This problem set explores the emerging paradigm of text diffusion models in large language models. Based on the video "Text diffusion: A new paradigm for LLMs", these problems will test your understanding of how diffusion-based LLMs differ from traditional auto-regressive models, their implementation challenges, and their potential advantages in speed, quality, and flexibility. The problems progress from basic concepts to advanced implementation details.
57 pts Hard 96 auto-regressive models diffusion models generative ai +7
American Invitational Mathematics Examination (AIME) Real Set 18
This problem set contains 5 questions from the AIME (American Invitational Mathematics Examination) validation dataset. These are competition-level mathematics problems that require numerical answers ranging from 000 to 999. ---
25 pts Hard 96 geometry vector geometry convex polygons +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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