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Free Problems
Chapter 10 - Agent Reasoning and Evaluation
This problem set covers key concepts from Chapter 10 on agent reasoning and evaluation. You'll explore various prompt engineering techniques including direct solution prompting, reasoning methods like chain of thought, and evaluation strategies for consistent solutions. The problems progress from foundational concepts to advanced applications, testing your understanding of how LLMs can be prompted to reason, plan, and evaluate solutions effectively.
25 pts Easy 95 prompt engineering few-shot prompting zero-shot prompting +7
Chapter 09 - Mastering Agent Prompts with Prompt Flow
This problem set covers key concepts from Chapter 9 on systematic prompt engineering and agent profile development using prompt flow. You'll explore agent profiles, personas, rubrics, grounding, and the iterative process of prompt evaluation. These problems test your understanding of how to systematically develop and evaluate effective AI agent prompts using Microsoft's prompt flow tool.
27 pts Medium 98 prompt engineering ai agents systematic prompting +7
Chapter 08 - Understanding Agent Memory and Knowledge
This problem set covers key concepts from Chapter 8 on agent memory and knowledge systems. You'll explore retrieval augmented generation (RAG), vector similarity search, document embeddings, LangChain implementations, and memory compression techniques. These problems test your understanding of how AI agents use memory and knowledge to enhance their contextual understanding and performance.
27 pts Medium 95 retrieval augmented generation agent memory knowledge retrieval +7
Chapter 07 - Assembling and Using an Agent Platform
This problem set covers key concepts from Chapter 7 of the O'Reilly book "AI Agents in Action" focusing on building agent platforms with Nexus and Streamlit. The problems test your understanding of agent profiles, personas, actions, tools, and the architecture of AI agent systems. You'll explore practical implementation details and theoretical concepts related to developing intelligent agent platforms.
27 pts Medium 95 agent platforms ai platforms langchain +7
Chapter 10 - Concluding Remarks on Reinforcement Learning for Finance
This problem set covers key concepts from Chapter 10 of "Reinforcement Learning for Finance" by Yves Hilpisch. The chapter provides concluding remarks on applying reinforcement learning to financial problems, discussing challenges like limited data availability, different RL approaches, and practical implementation considerations. These problems test your understanding of the core concepts, applications, and limitations discussed in the final chapter.
33 pts Medium 94 reinforcement learning finance applications domain-specific challenges +7
Optimal Execution in Financial Markets
This problem set covers the Almgren-Chriss (AC99) model for optimal execution of large block trades. The problems test understanding of market impact modeling, execution costs, risk aversion effects, and reinforcement learning approaches for optimal trading strategies. Questions progress from fundamental concepts to advanced analytical applications based on the O'Reilly Chapter 9 content.
40 pts Medium 98 execution costs financial markets almgren-chriss model +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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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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