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Free Problems
Chapter 12 - Numeric Python
This problem set covers key concepts from Chapter 12: Data Processing and Analysis, focusing on the Pandas library for data manipulation and the Seaborn library for statistical visualization. The problems progress from basic Series and DataFrame operations to advanced time series analysis and statistical plotting techniques.
28 pts Medium 102 pandas series indexing +7
Chapter 10 - Numeric Python
This problem set covers key concepts from Chapter 10: Sparse Matrices and Graphs. You'll practice working with sparse matrix representations, linear algebra operations, and graph algorithms using SciPy and NetworkX. The problems progress from basic concepts to advanced applications, testing your understanding of when and how to use sparse matrices effectively.
28 pts Medium 103 sparse-matrices scipy linear-algebra +7
Chapter 09 - Numeric Python
This problem set covers key concepts from Chapter 9 on Ordinary Differential Equations (ODEs), including symbolic solutions using SymPy, numerical integration methods, and practical applications of ODE solvers in SciPy. The problems progress from basic concepts to advanced implementations, testing understanding of both theoretical foundations and practical computational techniques.
28 pts Medium 97 ode-conversion first-order-systems numerical-methods +7
Chapter 08 - Numeric Python
This problem set covers key concepts from Chapter 8 on Integration, including numerical quadrature methods, symbolic integration, multiple integration, and integral transforms. The problems progress from basic concepts to advanced applications, testing understanding of both theoretical foundations and practical implementation using Python libraries like SciPy, SymPy, and mpmath.
28 pts Medium 97 quadrature-rules newton-cotes polynomial-degree +7
Chapter 07 - Numeric Python
This problem set covers interpolation concepts from Chapter 7, including polynomial interpolation, spline interpolation, and multivariate interpolation using NumPy and SciPy. The problems test understanding of interpolation theory, implementation details, and practical applications with Python.
34 pts Medium 94 interpolation curve-fitting concepts +7
Chapter 06 - Numeric Python
This problem set covers key concepts from Chapter 6 on Optimization, including univariate and multivariate optimization, constrained optimization, nonlinear least squares, and linear programming. The problems test understanding of optimization methods, their applications, and implementation using Python libraries like SciPy and CVXOPT.
34 pts Medium 101 optimization-classification nonlinear-programming constraints +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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