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Hello Interview
Accenture
Activision
Adobe
Airbnb
Airtable
Alteryx
Amazon
Amd
Anthropic
Apple
Atlassian
Bank Of America
Binance
Bitbucket
Blizzard
Box
Broadcom
Bytedance
Canonical
Canva
Circle
Cisco
Coinbase
Company
Crowdstrike
Databricks
Datadog
Deepmind
Dell
Discord
Docker
Doordash
Dropbox
Ea
Ebay
Elastic
Electronic Arts
Epic Games
Etsy
Expedia
Facebook
Figma
Flipkart
Github
Gitlab
Goldman Sachs
Google
Grubhub
Hashicorp
Hello Interview
Free Problems
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Understanding Reasoning vs Generic LLMs
This problem set explores the key differences between reasoning and generic large language models (LLMs) based on the video "What is the difference between Reasoning and Generic LLMs?". The problems cover fundamental concepts, practical applications, and analytical comparisons between these two types of AI models.
12 pts Easy 104 reasoning-questions llm-types basic-concepts +7
Chain of Thought Reasoning Fundamentals
This problem set covers the key concepts from Lecture 2 on Chain of Thought Reasoning, including inference time compute scaling, few-shot prompting, zero-shot reasoning, and the emergent reasoning abilities in large language models. The problems progress from basic concepts to advanced analytical questions based on the video content.
34 pts Medium 101 inference-time-compute reasoning-llms computational-resources +7
Megatron-LM Model Parallelism Concepts
This problem set covers key concepts from the ML Performance Reading Group Session 8 on Megatron-LM, focusing on tensor parallelism, communication patterns, and memory optimization strategies for training large language models. The problems progress from fundamental concepts to advanced implementation details discussed in the video.
44 pts Medium 94 tensor-parallelism pipeline-parallelism model-partitioning +7
Hands-on 13: Building Federated Learning with FedAvg, FedProx, FedDANE & FedSGD
This problem set covers the core concepts of federated learning as implemented in the tutorial video. You'll explore different federated learning algorithms (FedAvg, FedProx, FedDANE, FedSGD), understand their mathematical foundations, and analyze their trade-offs in terms of computation, communication, and handling of non-IID data. The problems progress from basic concepts to advanced implementation details.
21 pts Medium 98 federated-learning privacy-preserving distributed-computing +7
Scalable and Distributed Deep Neural Networks Practice
This problem set covers key concepts from the tutorial "Principles and Practice of Scalable and Distributed Deep Neural Networks". The problems test understanding of distributed training, parallelism strategies, communication patterns, and inference optimization techniques discussed in the video.
27 pts Medium 95 dnn-training forward-pass backward-pass +7
Inference Workflows and AI Service Architecture
This problem set covers key concepts from the "Inference Workflows" video, focusing on ALCF's inference service architecture, model deployment strategies, authentication methods, and agentic AI workflows. The problems test understanding of how scientific communities can leverage large language models through various interfaces and workflows.
34 pts Medium 101 inference-service architecture scientific-computing +7
Premium Problems
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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 2 Numeric Python (NPSCDS)
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 99 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

Knowledge Graphs

USA AI Olympiad

Explore competitive programming and AI contest preparation concepts

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Grade 5 Math

Discover elementary mathematics concepts and learning paths

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Featured Docs

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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
240 questions 684 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

beginner 459 easy 217 fundamental-concepts 112 optimization 98 performance 87 medium 85 machine-learning 84 fundamentals 83 distributed-systems 83 basic-concepts 80 linear-algebra 78 architecture 76 classification 73 data-structures 73 time-complexity 66 system-design 66 scalability 65 calculation 63 neural-networks 62 machine learning 61
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