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Free Problems
5 Querying Databases with SQL
This problem set covers essential SQL concepts from O'Reilly's chapter on database querying. You'll practice fundamental SQL operations including SELECT statements, filtering with WHERE, aggregation with GROUP BY and HAVING, conditional logic with CASE WHEN, subqueries, CTEs, table joins, and window functions. These problems progress from basic to advanced difficulty to test your comprehensive understanding of SQL querying.
30 pts Medium 93 where-clause null-handling logical-operators +7
8 Mining Data with Probability and Statistics
This problem set covers key concepts from Chapter 8: Mining Data with Probability and Statistics. You'll practice descriptive statistics, sampling, probability distributions, hypothesis testing, and error analysis. These problems test your understanding of fundamental statistical concepts essential for data science interviews and real-world data analysis.
28 pts Medium 96 descriptive-statistics central-tendency outliers +7
Exploring Today's Modern Data Science Landscape
This problem set covers key concepts from Chapter 1: "Exploring Today's Modern Data Science Landscape." The questions test your understanding of data science definitions, processes, career paths, required skills, and the evolving nature of the field. Each question is designed to help you master the fundamental concepts needed for data science interviews and practical applications.
26 pts Easy 96 data-science-definition fundamentals decision-making +7
Chapter 2. Transformer Architecture
This problem set covers key concepts from Chapter 2 of the O'Reilly book on Transformer Architecture. The problems test understanding of transformer components, attention mechanisms, positional encodings, and architectural design choices. Questions progress from fundamental concepts to advanced analytical thinking about transformer design and implementation.
30 pts Medium 93 transformer-architecture encoder-decoder model-types +7
Chapter 1. Prompt Engineering
This problem set covers the fundamental concepts and techniques of prompt engineering as presented in Chapter 1 of the O'Reilly AI Engineering Interviews book. You'll explore different prompting strategies, understand their applications, and learn how to design effective prompts for various use cases.
23 pts Medium 97 prompt-engineering hard-prompting soft-prompting +7
Analytic Geometry
This problem set covers key concepts from Analytic Geometry, including complex numbers, polar coordinates, and conic sections. These problems test your understanding of coordinate systems, geometric transformations, and the relationships between algebraic and geometric representations.
8 pts Medium 96 complex-numbers arithmetic-operations modulus +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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Cover of System Design Interview: An Insider's Guide Volume 2
System Design Interview: An Insider's Guide Volume 2
116 questions 348 pts
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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
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Featured Books

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

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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
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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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