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Free Problems
Chapter 05 - Numeric Python
This problem set covers key concepts from Chapter 5 on Equation Solving, including linear equation systems, matrix properties, eigenvalue problems, and nonlinear equation solving using both symbolic (SymPy) and numerical (SciPy) approaches. The problems progress from basic concepts to advanced applications, testing your understanding of both theoretical foundations and practical implementations.
27 pts Medium 102 linear-algebra matrix-rank square-systems +7
Chapter 04 - Numeric Python
This problem set covers key concepts from Chapter 4: Plotting and Visualization, focusing on Matplotlib fundamentals, figure and axes management, plot customization, and advanced visualization techniques. The problems progress from basic concepts to advanced applications, testing your understanding of Matplotlib's object-oriented API, plot types, axis customization, and 3D visualization.
30 pts Medium 95 matplotlib api object-oriented +7
Chapter 03 - Numeric Python
This problem set covers key concepts from Chapter 3: Symbolic Computing with SymPy. The problems test understanding of symbolic mathematics, expression manipulation, calculus operations, and linear algebra using SymPy. Questions progress from basic symbol creation to advanced analytical computations.
26 pts Easy 101 symbolic-computing sympy symbol-creation +7
Chapter 01 - Numeric Python
This problem set covers key concepts from Chapter 1: Introduction to Computing with Python. The problems test understanding of Python environments, IPython features, Jupyter Notebook functionality, and the scientific computing ecosystem. Questions progress from basic concepts to advanced applications of the tools discussed in the chapter.
23 pts Medium 102 programming-languages trade-offs development-time +7
Chapter 10 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 10: Exploring Existing GPU Models. The chapter discusses using GPU libraries like cuBLAS and Thrust, deciding when to write custom kernels, strategies for moving sequential code to GPU, and testing approaches using GTest and Pytest. These problems test your understanding of library usage, performance considerations, and testing methodologies in GPU programming.
29 pts Medium 103 cublas matrix-multiplication memory-layout +7
Chapter 09 - GPU Programming with C++ and CUDA
This problem set covers key concepts from Chapter 9: "Exposing Your Code to Python" about integrating GPU-accelerated C++ libraries with Python. The problems test understanding of ctypes, Python extensions, memory management, performance considerations, and practical implementation details for bridging C++/CUDA code with Python.
30 pts Medium 98 python-integration gpu-programming ctypes +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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Deep Reinforcement Learning Hands-On - Third Edition
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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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