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Free Problems
Introduction to PyTorch
This problem set contains **10 advanced multiple-choice questions** designed to test practical PyTorch knowledge, including tensors, autograd, optimizers, neural networks, and training loops. Each question includes detailed explanations for deeper understanding.
19 pts Easy 96 pytorch autograd deep learning +7
Distributed Inference with Transformers
This problem set focuses on **distributed inference strategies** for transformers, including: - Tensor parallelism (splitting layers across GPUs) - Pipeline parallelism (splitting layers by stages across GPUs) - KV cache sharding (distributed memory across devices) - Scheduling and serving architectures for efficient inference You will answer **15 questions** mixing multiple-choice, math, and code.
40 pts Medium 93 transformers distributed inference tensor parallelism +7
PyTorch Training Loop Practice
This problem set contains **5 questions** of mixed types (multiple-choice and code) to test your ability to build a simple training loop in PyTorch. You will work through defining tensors, creating models, writing training loops, and evaluating performance. **Environment:** - Python 3.10+ - PyTorch 2.0+ Answer carefully. Code questions require you to complete the Python starter code.
10 pts Easy 107 pytorch gpu computing tensor creation +7
Blockchain Fundamentals and Advanced Concepts
This problem set contains 15 questions covering blockchain concepts, from basic structures to advanced consensus mechanisms, cryptography, and applications.
30 pts Easy 97 blockchain block cryptocurrency +7
Multimodal Inference Acceleration
This problem set focuses on **inference acceleration in multimodal (vision-language) models**, including: - Visual feature extraction and fusion with text - Multimodal KV cache handling - Cross-modal projection and alignment - Efficient serving and batching for multimodal requests You will answer **15 questions** mixing multiple-choice, math, and code.
41 pts Medium 96 vision-language models visual encoders multimodal models +7
Probability Distributions Challenge Set
This problem set contains 15 questions covering probability distributions: discrete distributions (Bernoulli, Binomial, Geometric, Poisson), continuous distributions (Uniform, Exponential, Normal), expectations, variances, cumulative distribution functions, moment generating functions, transformations, and properties of joint distributions. The questions progress from fundamental formulas to advanced reasoning and coding exercises, testing diverse aspects of probability distributions.
35 pts Easy 95 probability distributions bernoulli distribution pmf +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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