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Free Problems
Quantization Fundamentals Practice
This problem set tests your understanding of quantization fundamentals as explained in the video "How LLMs survive in low precision | Quantization Fundamentals". You'll explore why quantization is necessary, when it's applied, and how the mathematical transformations work between floating-point and integer representations. The problems progress from basic concepts to advanced quantization arithmetic.
26 pts Medium 101 quantization large language models model efficiency +7
Place Value Pioneers: Navigating the Number System with Number Sense
This problem set takes students on a journey through our place value system, building strong number sense by exploring how numbers are structured, compared, and operated on. Students will work with whole numbers, decimals, and fractions while applying place value understanding to solve real-world problems involving measurement, geometry, and data analysis. The mixed difficulty problems progressively build from basic place value concepts to more complex applications across multiple mathematical domains.
16 pts Easy 97 measurement plant growth number sense +6
The KV Cache: Memory Usage in Transformers
This problem set explores the KV (Key-Value) cache mechanism in Transformer models, which is crucial for understanding memory usage during text generation. The problems cover concepts from basic self-attention mechanics to advanced memory calculations, based on the YouTube video "The KV Cache: Memory Usage in Transformers".
29 pts Medium 102 transformers self-attention neural networks +7
Cache-Augmented Generation vs Traditional RAG Systems
This problem set explores the revolutionary Cache-Augmented Generation (CAG) methodology that challenges traditional Retrieval-Augmented Generation (RAG) systems. Based on the video "Goodbye RAG - Smarter CAG w/ KV Cache Optimization," these problems examine how key-value cache optimization enables retrieval-free knowledge integration, leveraging extended context windows of modern LLMs. The problems progress from fundamental concepts to advanced architectural implications, requiring synthesis of transformer mechanics, attention mechanisms, and system optimization strategies.
69 pts Expert 95 transformer models auto-regressive generation computational complexity +7
Speculative Decoding - When Two LLMs are Faster than One
This problem set explores the key concepts from the video "Speculative Decoding: When Two LLMs are Faster than One" which introduces a technique for speeding up transformer inference using two language models. The problems cover the algorithm, mathematical foundations, and practical considerations of speculative decoding as explained in the Deep Mind and Google papers.
33 pts Medium 95 speculative decoding large language models llm optimization +7
🎶Learn AI via Pop Music - CNN
This problem set covers fundamental concepts in Convolutional Neural Networks (CNNs) based on the YouTube song "CNN". The problems progress from basic CNN components to advanced architectures and techniques, testing your understanding of convolution operations, network architectures, optimization methods, and computer vision applications. Each question is directly related to concepts mentioned in the song transcript.
30 pts Medium 99 convolutional neural networks image recognition spatial operations +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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