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Free Problems
Advanced Quantization Techniques
This problem set focuses on **advanced quantization techniques** used for efficient LLM inference, including: - RTN (Round-to-Nearest) quantization - AWQ vs GPTQ comparison - GPTQ internals: Hessian approximation and Cholesky decomposition - GWQ (Group-wise Quantization) - llama.cpp quantization methods (q4, q5, qX_k) You will answer **15 questions** mixing multiple-choice, math, and code.
89 pts Hard 90 quantization machine learning numerical computing +7
DeepSeek Models and Technologies
This problem set covers DeepSeek's recent open-source models and innovations, including V3, R1, R1-Zero, and Distill. It tests your understanding of their Mixture-of-Experts (MoE) architecture, Multi-Head Latent Attention (MLA), reinforcement learning approaches, distillation strategies, and positional encoding methods (e.g., RoPE). The tasks include conceptual, numerical, mathematical, and coding questions.
62 pts Medium 94 deep learning large language models model architecture +7
Advanced Regularization Techniques
This problem set covers advanced regularization techniques in deep learning, including Dropout, Weight Decay, Label Smoothing, Mixup, and Stochastic Depth. You will encounter conceptual, numerical, mathematical, and coding questions designed to test both your understanding and your implementation skills.
48 pts Medium 92 deep learning regularization overfitting +7
Inference Acceleration for Transformers
This problem set focuses on **inference acceleration techniques** in Transformers, including: - KV cache storage, update, and quantization - Speculative decoding (draft + target model) - Continuous batching for dynamic request scheduling - Prefill/Decode phase separation You will answer **15 questions** mixing multiple-choice, math, and code.
38 pts Medium 94 transformer models inference optimization natural language processing +7
Transformer Architectures Basics
This problem set covers different Transformer architectures (Encoder-only, Decoder-only, Encoder-Decoder) and various positional encoding strategies (Sinusoidal, Learned, Rotary, Relative). You will encounter conceptual, numerical, mathematical, and coding questions designed to test both your understanding and implementation skills.
47 pts Medium 94 transformer architectures attention mechanisms deep learning +7
ResNet and U-Net Architectures
In this problem set, you will explore advanced convolutional neural network architectures, focusing on two influential designs: **ResNet** (Residual Networks) and **U-Net**. You will answer questions ranging from conceptual understanding of residual connections to detailed coding tasks involving forward passes and architectural modifications. This will test your ability to reason about modern deep learning architectures that are widely used in computer vision, medical imaging, and beyond.
39 pts Medium 101 neural networks residual connections deep learning +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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