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Free Problems
Chapter 02 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 2: Setting Up Your Development Environment for CUDA programming. The questions test understanding of NVIDIA driver installation, Docker configuration, CUDA Toolkit setup, and the trade-offs between different development environment approaches. All questions are based directly on the chapter content and progress from basic concepts to advanced analytical thinking.
24 pts Medium 97 nvidia-driver cuda-requirements gpu-programming +7
Chapter 01 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 1: Introduction to Parallel Programming. The questions test your understanding of parallel programming fundamentals, GPU architecture, and the differences between CPUs and GPUs. Work through these problems to reinforce your knowledge of when and how to use parallelism effectively.
22 pts Medium 104 parallel-programming fundamentals core-concepts +7
The End of Training (PDLT)
This problem set explores the finite-width training dynamics of deep neural networks, covering ddNTKs, algorithm dependence, and the theoretical framework for understanding fully-trained networks. The problems progress from basic concepts to advanced analytical reasoning about representation learning and optimization algorithms.
39 pts Hard 100 ddntk finite-width training-dynamics +7
11 Representation Learning (PDLT)
This problem set covers key concepts from Chapter 11 on Representation Learning, focusing on finite-width neural networks, the differential of the Neural Tangent Kernel (dNTK), and how these enable feature learning beyond the infinite-width limit. Problems progress from fundamental concepts to advanced analytical applications.
24 pts Medium 103 representation-learning finite-width-networks dntk +7
10 Kernel Learning (PDLT)
This problem set covers key concepts from Chapter 10 on Kernel Learning, focusing on infinite-width neural networks, the Neural Tangent Kernel (NTK), gradient-based learning, and the connections between kernel methods and linear models. Problems progress from fundamental concepts to advanced analytical applications.
39 pts Hard 100 neural-tangent-kernel infinite-width representation-learning +7
9 Effective Theory of the NTK at Initialization (PDLT)
This problem set covers the key concepts from Chapter 9: Effective Theory of the NTK at Initialization. The problems test understanding of NTK criticality analysis, scaling laws, universality classes, and the relationship between initialization hyperparameters and training dynamics. Questions progress from basic concepts to advanced analytical derivations.
34 pts Hard 95 ntk frozen-ntk infinite-width-limit +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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Featured PDFs

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Hands-On Machine Learning with Scikit-Learn and PyTorch
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Deep Reinforcement Learning Hands-On - Third Edition
222 questions 720 pts

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Attention in transformers, step-by-step | Deep Learning Chapter 6
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Knowledge Distillation: How LLMs train each other
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Diffusion Model
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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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