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Free Problems
Chapter 09 - Ways to Speed Up RL - Chapter 9 Practice
This problem set covers key concepts from Chapter 9 "Ways to Speed Up RL" which focuses on engineering optimizations to improve reinforcement learning training performance. The problems test understanding of computation graphs, parallel processing, environment wrappers, and performance benchmarking in RL systems.
26 pts Medium 99 pytorch deep learning reinforcement learning +7
Chapter 08 - DQN Extensions and Improvements
This problem set covers the key DQN extensions and improvements discussed in Chapter 8, including N-step DQN, Double DQN, Noisy Networks, Prioritized Replay Buffer, Dueling DQN, and Categorical DQN. These methods address various challenges in deep reinforcement learning such as convergence speed, overestimation bias, exploration efficiency, and distributional value learning.
45 pts Hard 92 reinforcement learning deep q-networks dqn extensions +7
Chapter 07 - Higher-Level RL Libraries with PTAN
This problem set covers key concepts from Chapter 7 on higher-level RL libraries, focusing on the PTAN library. You'll be tested on action selectors, agents, experience sources, replay buffers, and other PTAN components that simplify RL implementation while maintaining flexibility. These problems progress from basic concepts to practical implementation details.
34 pts Medium 103 reinforcement learning q-learning ptan +7
Chapter 06 - Deep Q-Networks Practice Problems
This problem set covers key concepts from Chapter 6 on Deep Q-Networks, including value iteration limitations, Q-learning, DQN architecture, and practical implementation details. The problems progress from fundamental concepts to advanced implementation details, testing your understanding of reinforcement learning with neural networks.
27 pts Medium 97 value iteration reinforcement learning atari games +7
Chapter 05 - Tabular Learning and Bellman Equation Practice
This problem set covers key concepts from Chapter 5 on Tabular Learning and the Bellman Equation. You'll practice calculating state values, understanding the Bellman equation, working with value iteration, and comparing V-learning vs Q-learning approaches. These problems test your understanding of fundamental reinforcement learning concepts that form the basis for more advanced methods like Deep Q-Networks.
26 pts Medium 99 reinforcement learning expected value policy evaluation +7
Chapter 04 - Cross-Entropy Method Reinforcement Learning
This problem set covers the Cross-Entropy Method in reinforcement learning as described in the O'Reilly book chapter. The problems test understanding of RL taxonomy, method implementation, practical applications, and theoretical foundations. Questions progress from basic concepts to advanced implementation details.
35 pts Medium 94 reinforcement learning cross-entropy method learning methods +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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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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