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Free Problems
Chapter 12 - Deep Computer Vision Using Convolutional Neural Networks
This problem set covers key concepts from Chapter 12 on Deep Computer Vision Using Convolutional Neural Networks. You'll explore CNN architectures, convolutional layers, pooling layers, transfer learning, and practical implementations using PyTorch. The problems progress from fundamental concepts to advanced applications, testing your understanding of how CNNs work and how to implement them effectively.
28 pts Medium 95 convolutional neural networks image classification deep learning +7
Chapter 11 - Training Deep Neural Networks
This problem set covers key concepts from Chapter 11 on training deep neural networks, including gradient problems, initialization strategies, activation functions, normalization techniques, optimization algorithms, learning rate scheduling, and regularization methods. These problems will test your understanding of the fundamental challenges in deep learning and the techniques used to overcome them.
19 pts Medium 98 neural networks gradient problems deep learning +7
Chapter 10 - PyTorch Fundamentals and Neural Network Implementation
This problem set covers key concepts from Chapter 10: Building Neural Networks with PyTorch. You'll practice working with PyTorch tensors, autograd, neural network modules, training loops, and model evaluation. The problems progress from basic tensor operations to advanced neural network architecture design.
26 pts Medium 102 pytorch tensor operations machine learning +7
Chapter 09 - Introduction to Artificial Neural Networks
This problem set covers key concepts from Chapter 9: Introduction to Artificial Neural Networks. You'll explore the fundamentals of neural networks, from biological inspiration to practical implementation with MLPs. The problems progress from basic concepts to advanced analytical thinking about neural network architecture and training.
24 pts Easy 98 neural networks mcculloch pitts artificial intelligence history +7
Chapter 07 — Dimensionality Reduction
This Chapter 07 problem set assesses your understanding of core dimensionality reduction concepts discussed in the chapter: the curse of dimensionality, projection vs. manifold learning, PCA (SVD, explained variance, choosing the number of components, compression, randomized and incremental variants), random projection (Johnson–Lindenstrauss bound), LLE (algorithmic steps and complexity), and other techniques (MDS, Isomap, t-SNE, LDA). Questions progress from foundational to advanced and mix conceptual, practical, analytical, and coding skills.
32 pts Medium 101 dimensionality reduction machine learning feature selection +7
Chapter 08 – Unsupervised Learning Techniques
This Chapter 08 practice set covers key unsupervised learning techniques from Hands-On Machine Learning with Scikit-Learn and PyTorch. You will answer conceptual, practical, and analytical questions on: clustering (k-means, k-means++), silhouette and inertia, selecting k, image segmentation with k-means, semi-supervised learning via label propagation, DBSCAN, Gaussian Mixtures (EM, AIC/BIC), and anomaly vs. novelty detection. Questions progress from basic to advanced to help you master the core ideas.
27 pts Medium 101 unsupervised learning machine learning clustering +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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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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