Введение в машинное обучение - Часть I
Изучите основы машинного обучения, включая ключевые концепции, алгоритмы и методы анализа данных для решения практических задач.
Data Shift and Model Adaptation in Machine Learning
Explore data shift principles, detection strategies, and adaptation techniques in machine learning for dynamic environments. Practice hands-on with advanced topics to enhance model performance.
ML-Based Time Series Regression - Concepts from Demand Forecasting
Explore ML-based time series regression for demand forecasting in retail. Learn to optimize replenishment and pricing using AI for accurate predictions and automated decision-making at scale.
Weighted Finite State Automata and Linear Algebra - JSALT 2023 Team Presentation
Explore weighted finite state automata and linear algebra in speech and language processing through a comprehensive JSALT 2023 team presentation by Lucas Ondel Yang.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 2
Explorez les dynamiques d'optimisation en très haute dimension et leur application en apprentissage automatique, en mettant l'accent sur les matrices aléatoires et les statistiques résumées.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 1
Explorez les algorithmes d'optimisation en haute dimension pour l'apprentissage automatique, en mettant l'accent sur la descente de gradient stochastique et les transitions spectrales des matrices aléatoires.
Geometric Probabilistic Models - Tutorial 4
Explore geometric probabilistic models for data-efficient, uncertainty-aware decision-making in applications like drug design and robotics. Learn theory and implementation of Gaussian processes and related techniques.
Online Optimization Meets Federated Learning - Tutorial
Explore state-of-the-art theoretical results in online optimization, federated learning, and their intersection. Gain insights into regret analysis, data heterogeneity, and distributed online optimization.
Introduction to Deep Generative Models
Explore deep generative models, their applications, and underlying principles in this comprehensive introduction by Jes Frellen at the Nordic Probabilistic AI School.
Gaussian Processes
Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.
Introduction to Probabilistic Models - Probabilistic AI School
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
Deep Generative Models 1 - Lecture
Explore deep generative models with experts, covering fundamental concepts and advanced techniques in probabilistic AI. Gain insights into cutting-edge research and applications.
Introduction to Probabilistic Models - Lecture 1
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
StructCurves - Interlocking Block-Based Line Structures
Discover innovative techniques for creating interlocking block structures through line-based design methods and computational approaches for stable construction assemblies.
From Observability to Enforcement: Lessons Learned Implementing eBPF Runtime Security
Dive into eBPF runtime security implementation, exploring the transition from observability to enforcement. Learn kernel-level security event collection, attack prevention strategies, and practical insights from the Tetragon team.
Comparing Kubernetes Security Agents: Falco, Tetragon and KubeArmor
Explore a detailed comparison of three leading eBPF-based Kubernetes security solutions, examining performance impacts, required privileges, and key features to make an informed choice for cluster protection.
Shopify's Open Source Approach to Network Monitoring with eBPF, Vector and ClickHouse
Discover how to build a scalable network monitoring system using eBPF, Vector, and ClickHouse to process millions of events per second across Kubernetes clusters with real-world implementation insights.
Machine Learning for Protein Engineering in Drug Discovery
Discover how machine learning intersects with molecular biology and protein engineering through insights from UC Berkeley Professor Jennifer Listgarten's keynote address on advancing drug discovery innovations.
Trajectory Inference in Wasserstein Space
Explore advanced trajectory inference methods using Wasserstein geometry and B-spline approximation for analyzing dynamic processes in computational biology and point cloud data.
Asymmetric Encryption: From RSA to Post-Quantum Cryptography
Delve into the mathematical foundations and practical applications of asymmetric encryption, exploring RSA, ECDSA, their limitations, and future developments in post-quantum cryptography.
Interpolation at Arbitrary Points in High-Order Meshes on GPUs
Discover advanced techniques for arbitrary point interpolation in high-order mesh computations, focusing on GPU optimization and parallel processing for efficient scientific simulations.