Online Workshop
Online Workshop Every Week

Join our free weekly interactive learning sessions.

Master AI/ML with instant feedback and personalized learning

"Cogito, ergo sum" (I think, therefore I am)

— René Descartes

René Descartes
Free Problems
Chapter 16 - Trust Region Methods in Reinforcement Learning
This problem set covers key concepts from Chapter 16 on Trust Region Methods in Deep Reinforcement Learning. You'll explore methods like PPO, TRPO, ACKTR, and SAC that aim to improve policy gradient stability through trust region optimization, KL divergence constraints, and entropy regularization. The problems test your understanding of the mathematical foundations, implementation details, and comparative performance of these advanced RL methods.
29 pts Medium 99 reinforcement learning policy gradient methods trust region methods +7
Chapter 15 - Continuous Action Space in Reinforcement Learning
This problem set covers key concepts from Chapter 15 on continuous action spaces in reinforcement learning. You'll explore the differences between discrete and continuous action spaces, understand various algorithms for continuous control (A2C, DDPG, D4PG), and analyze their implementations and trade-offs. These problems test your understanding of policy representation, exploration strategies, and algorithm architectures in continuous control problems.
29 pts Medium 97 reinforcement learning action spaces continuous control +7
Chapter 14 - Web Navigation and Browser Automation with RL
This problem set covers key concepts from Chapter 14 on Web Navigation and Browser Automation using Reinforcement Learning. You'll explore MiniWoB++ benchmark environments, action spaces, observation spaces, and practical challenges in applying RL to web navigation tasks. The problems progress from basic concepts to advanced implementation details.
41 pts Medium 98 web navigation reinforcement learning browser automation +7
Chapter 13 - TextWorld Environment and RL for Interactive Fiction
This problem set covers key concepts from Chapter 13 on using Reinforcement Learning with the TextWorld environment for text-based interactive fiction games. You'll explore environment setup, NLP preprocessing techniques, DQN architectures for text-based games, and modern approaches using transformers and LLMs. These problems test understanding of RL applications in complex text-based environments with rich observation spaces.
29 pts Medium 95 reinforcement learning text world games interactive fiction +7
Chapter 12 - Actor-Critic Methods and A2C/A3C
This problem set covers key concepts from Chapter 12 on Actor-Critic methods, including A2C and A3C. These methods combine policy-based and value-based approaches to improve stability and convergence in deep reinforcement learning. The problems test understanding of variance reduction, advantage calculation, network architectures, and parallelization strategies.
28 pts Medium 101 reinforcement learning policy gradients baseline methods +7
Chapter 11 - Policy Gradients Fundamentals
This problem set covers key concepts from Chapter 11 on Policy Gradients, including the REINFORCE algorithm, policy representation, advantages of policy-based methods, and practical implementation considerations. These problems test understanding of both theoretical foundations and practical applications of policy gradient methods in reinforcement learning.
29 pts Medium 96 policy gradients reinforcement learning discrete actions +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

Knowledge Graphs

USA AI Olympiad

Explore competitive programming and AI contest preparation concepts

View Full Graph
Grade 5 Math

Discover elementary mathematics concepts and learning paths

View Full Graph

Featured PDFs

View All PDFs
Cover of System Design Interview: An Insider's Guide Volume 2
System Design Interview: An Insider's Guide Volume 2
116 questions 348 pts
Cover of System Design Interview: An Insider's Guide
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

View All Books
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

Featured Videos

View All Videos
Cover of Flow-Matching vs Diffusion Models explained side by side
Flow-Matching vs Diffusion Models explained side by side
10 questions 29 pts
Cover of Attention in transformers, step-by-step | Deep Learning Chapter 6
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
Click on any tag to filter problems by that topic