Online Workshop
Online Workshop Every Week

Join our free weekly interactive learning sessions.

Master AI/ML with instant feedback and personalized learning

"Cogito, ergo sum" (I think, therefore I am)

— René Descartes

René Descartes
Free Problems
Chapter 17 - Advanced Transformer Techniques
This problem set covers advanced techniques for improving transformer performance, including acceleration methods, handling long sequences, and alternative architectures. Based on Chapter 17 of "Hands-On Machine Learning with Scikit-Learn and PyTorch" by Aurélien Géron, these questions test your understanding of key concepts like attention optimization, positional encodings, and state-space models.
37 pts Medium 102 transformers computational challenges deep learning +7
Chapter 16 - Vision and Multimodal Transformers
This problem set covers key concepts from Chapter 16 on Vision and Multimodal Transformers. You'll explore vision transformers (ViTs), their hierarchical variants, self-supervised learning techniques, and multimodal architectures that combine vision with other modalities like text and audio. The problems progress from fundamental concepts to advanced applications, testing your understanding of transformer architectures beyond natural language processing.
39 pts Medium 93 vision transformers self-attention image processing +7
Chapter 15 - Transformer Architecture and Applications
This problem set covers key concepts from Chapter 15 on Transformers for Natural Language Processing and Chatbots. You'll explore the Transformer architecture, multi-head attention, encoder-decoder models, and practical applications like chatbots and fine-tuning techniques. The problems progress from fundamental concepts to advanced applications, testing your understanding of how transformers work and how they're used in modern NLP systems.
29 pts Medium 96 transformer architecture attention mechanisms deep learning +7
Chapter 14 - Natural Language Processing with RNNs and Attention
This problem set covers key concepts from Chapter 14 on Natural Language Processing with RNNs and Attention. You'll explore character RNNs, tokenization techniques, embeddings, sentiment analysis, encoder-decoder models, and attention mechanisms. These problems test your understanding of both theoretical concepts and practical implementations using PyTorch and Hugging Face libraries.
26 pts Medium 100 natural language processing rnn training text generation +7
Chapter 13 - Processing Sequences Using RNNs and CNNs
This problem set covers key concepts from Chapter 13 on processing sequences using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). You'll explore RNN architectures, training methods, time series forecasting, and advanced sequence processing techniques including LSTM, GRU, and WaveNet. These problems test your understanding of sequence modeling fundamentals and practical applications.
28 pts Medium 99 recurrent neural networks rnn weight matrices neural network parameters +7
Chapter 13 - Processing Sequences Using RNNs and CNNs
This problem set covers key concepts from Chapter 13 on processing sequences using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). You'll explore RNN architectures, training methods, time series forecasting, and advanced sequence processing techniques including LSTM, GRU, and WaveNet. These problems test your understanding of sequence modeling fundamentals and practical applications.
29 pts Medium 96 recurrent neural networks rnn architecture neural network parameters +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

Knowledge Graphs

USA AI Olympiad

Explore competitive programming and AI contest preparation concepts

View Full Graph
Grade 5 Math

Discover elementary mathematics concepts and learning paths

View Full Graph

Featured PDFs

View All PDFs
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

View All Books
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

View All Videos
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
Click on any tag to filter problems by that topic