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Free Problems
Chapter 1 Proximity Service (SDIIGV)
This problem set covers key concepts from Chapter 1 on Proximity Service design, including geospatial indexing algorithms, system architecture, API design, and scalability considerations for location-based services. The problems test understanding of geohash, quadtree, caching strategies, and trade-offs in proximity service implementation.
32 pts Medium 95 functional-requirements proximity-service system-design +7
Optimization Algorithms for Deep Learning
This problem set covers optimization algorithms for deep learning, including Stochastic Gradient Descent, Momentum, Nesterov Momentum, AdaGrad, RMSprop, and Adam. These algorithms are crucial for efficiently training neural networks by adapting the learning process to the loss landscape. Understanding their mathematical formulations, advantages, and limitations is essential for effective deep learning practice.
29 pts Medium 99 stochastic-gradient-descent batching computational-efficiency +7
Part 1 (UUDFCG)
This problem set covers the UniCalli framework for unified diffusion-based Chinese calligraphy generation and recognition. The problems test understanding of the core concepts, architecture, training methodology, and experimental results presented in the chapter. Questions progress from fundamental concepts to advanced analytical applications.
38 pts Medium 96 unified-framework mutual-enhancement core-motivation +7
Python I/O and Data Pipeline Assessment - Part 5
20 questions focused on advanced PyTorch data pipeline topics: image loaders (PIL vs OpenCV), memmap datasets, samplers, multi-file samples, dataset integrity/error handling, TorchScript-friendly transforms, throughput benchmarking, GPU prefetch patterns, checkpointing sampler state, webdataset/fsspec streaming, backpressure, GIL, asyncio, dataset structuring, and small ETL tasks. Numpy version: 2.3; PyTorch version: 2.8; Pandas: 2.3
20 pts Medium 103 PIL.Image cv2 image-io +7
Python I/O and Data Pipeline Assessment - Part 3
20 questions focused on encoding detection, CSV robustness, columnar formats (Parquet/Feather/Arrow), JSON ingestion/validation, caching and checksum integrity, range requests, and efficient concatenation without dtype surprises. You can assume python libraries are imperted and `np` is `numpy` and `pd` is `pandas`.
20 pts Medium 99 chardet charset_normalizer encoding-detection +7
Python I/O and Data Pipeline Assessment - Part 2
20 questions focused on CSV/JSON handling, compression and archives, memory mapping, in-memory file objects, robust downloads, and safe file writes/locking.
59 pts Medium 96 csv.reader csv newline-handling +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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