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Free Problems
Chapter 04 - Multi-Agent Systems with AutoGen and CrewAI
This problem set covers key concepts from Chapter 4 of "AI Agents in Action" focusing on multi-agent systems using AutoGen and CrewAI. You'll explore agent communication patterns, skill integration, observability, and practical implementation of multi-agent systems for various tasks including code generation and collaborative problem solving.
34 pts Medium 100 autogen multi-agent systems agent communication +7
Chapter 03 - GPT Assistants Practice Problems
This problem set tests your understanding of GPT assistants from Chapter 3 of "AI Agents in Action". The questions cover key concepts including GPT assistant creation, code interpretation, custom actions, knowledge extension through file uploads, and publishing considerations. Work through these problems to reinforce your understanding of building and deploying AI assistants using the OpenAI GPT platform.
24 pts Easy 98 gpt assistants ai capabilities chatbots +7
Chapter 02 - LLM Fundamentals and Applications
This problem set covers fundamental concepts about Large Language Models (LLMs) including their architecture, usage, prompt engineering techniques, and practical considerations for deployment. The problems test understanding of generative vs predictive models, OpenAI API usage, LM Studio, prompt engineering strategies, and LLM selection criteria.
27 pts Medium 100 generative models predictive models machine learning +7
Chapter 01 - AI Agents and Their World
This problem set covers fundamental concepts about AI agents, their components, types, and applications. Based on Chapter 1 of "AI Agents in Action," these questions test your understanding of agent definitions, component systems, and the evolving landscape of AI interfaces. Work through these problems to master the core concepts of AI agents.
16 pts Medium 104 ai agents agent definition ai fundamentals +7
Chapter 17 - Black-Box Optimization Methods in Reinforcement Learning
This problem set explores black-box optimization methods in reinforcement learning, including Evolution Strategies (ES) and Genetic Algorithms (GA). These methods treat the optimization objective as a black box without assumptions about differentiability or smoothness, making them highly parallelizable and applicable to non-smooth reward functions. The problems cover conceptual understanding, implementation details, and analytical comparisons between different approaches.
44 pts Medium 98 reinforcement learning black-box optimization gradient-based methods +7
Chapter 18 - Advanced Exploration in Reinforcement Learning
This problem set covers advanced exploration techniques in reinforcement learning, focusing on why traditional methods like Ξ΅-greedy can be insufficient and exploring alternative approaches like noisy networks, count-based methods, and prediction-based methods. The problems test understanding of exploration challenges, method implementations, and experimental results from the MountainCar and Atari environments.
54 pts Hard 95 reinforcement learning exploration exploitation sparse rewards +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
107 questions 418 pts

Featured Books

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