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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 90 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 94 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 95 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 92 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 95 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 95 complex-numbers arithmetic-operations modulus +7
Premium Problems
IAIO 2024 Part 1 - Evaluation Metrics, Ethics, and AI Applications
This problem covers the first two categories of the 2024 International Artificial Intelligence Olympiad (IAIO), focusing on evaluation metrics and AI ethics. You'll work through real-world scenarios including medical diagnosis with rare diseases, spam email detection, and various ethical dilemmas in AI applications. The problems cover: - Why accuracy fails with class imbalance - Choosing appropriate evaluation metrics (precision, recall, F1-score) - Ethical dilemmas in AI (trolley problem, algorithmic bias) - Fairness and transparency in AI systems - Copyright and data usage in AI training
20.00 45 pts Medium 96 evaluation metrics class imbalance medical diagnosis +7
Why Skip Connection Matters: From MLP to ResNet to Transformer
In this problem set, you will explore why Residual Networks (ResNets) revolutionized deep learning by overcoming the vanishing and exploding gradient problems in deep networks. We'll start by analyzing a simple Multi-Layer Perceptron (MLP) with LayerNorm and ReLU, walk through the gradient propagation mathematically, and progressively extend it to deep networks. You will be asked to prove, derive, and understand the mathematical behavior of gradients in deep models. Then, we will introduce the skip connections in ResNet and demonstrate mathematically why they stabilize the training. Finally, you will be asked to prove the theoretical results step-by-step and apply your understanding. This exercise is foundational for understanding modern deep architectures such as ResNet, UNet, and Transformer blocks.
9.99 14 pts Medium 99

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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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