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Free Problems
Chapter 01 - Deep Reinforcement Learning Hards-On Chapter 1
This problem set covers the fundamental concepts of reinforcement learning from Chapter 1, including the differences between RL and other ML paradigms, Markov processes, reward systems, and the core components of RL systems. These questions test your understanding of the theoretical foundations that underpin modern reinforcement learning approaches.
13 pts Medium 104 reinforcement learning supervised learning unsupervised learning +7
FlashAttention
This problem set covers the **FlashAttention 1, 2, and 3** algorithms, focusing on theoretical and practical aspects: - Online softmax computation - Block-sparse and tiled attention - Complexity analysis and memory savings - New features in FlashAttention-2 and FlashAttention-3 (parallelism, sequence reordering, head grouping) You will answer **10 questions** mixing multiple-choice, math, and code.
35 pts Medium 101 attention mechanisms memory optimization deep learning +7
Flow Matching in Generative Modeling
This notebook contains 15 challenging problems, designed to test and deepen understanding of flow matching techniques in generative modeling. Questions span conceptual, mathematical, and coding aspects of flow matching, including ODE/SDE formulations, training objectives, probability flows, and algorithmic implementations.
50 pts Medium 98 generative modeling flow matching deep learning +7
Training Optimization Techniques
This problem set focuses on **training optimization techniques** in large-scale deep learning, including: - Gradient checkpointing - ZeRO optimizer stages - LoRA (Low-Rank Adaptation) - Mixed precision training - Optimizer design and tricks You will answer **15 questions** mixing multiple-choice, value, text, math, and code.
40 pts Medium 95 deep learning gradient computation training optimization +7
Understanding Transformers
This problem set covers the foundations of the Transformer architecture, including Self-Attention, Multi-Head Attention, Positional Encoding, Feedforward Networks, and Encoder Layer design. It includes conceptual, numerical, mathematical, and coding questions to test your understanding and implementation skills.
43 pts Medium 98 transformer models neural networks sequence modeling +7
Advanced Topics in Flow Matching in Generative Models
This problem set explores advanced concepts in machine learning with a focus on generative models, flow-based methods, diffusion models, conditional generation, and optimization techniques. The questions are designed to test both theoretical understanding and practical implementation skills, emphasizing deterministic reasoning accessible by hand computation where possible. Key topics covered: - Generative Models (Variational Inference, Diffusion Models) - Flow Matching and MeanFlow - Conditional Generation and Image Synthesis - Optimization in latent space and guidance schemes - Guidance Scales and their role in control generation All answers must be deterministic, and no information about the answer should be leaked in the question text.
137 pts Hard 83 flow matching differential equations generative models +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
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

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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

Featured Videos

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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
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
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