How Can AI Help Humanity? - With Nick Jennings
Explore AI's potential to aid humanity through human-machine partnerships, examining applications in disaster response, medical diagnosis, and future job impacts.
Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems
Explore state-of-the-art machine learning techniques and their applications in finance, healthcare, and recommendation systems, focusing on ensemble methods, collaborative filtering, and portfolio construction.
KDD 2020- Learning by Exploration-Part 2
Explore advanced techniques in online learning, including graph Laplacian regularization, social influence modeling, adaptive clustering, and particle Thompson sampling for improved exploration and personalization.
Challenges and Solutions in Industry Scale Data and AI Systems - Yangqing Jia
Explore industry-scale data and AI systems challenges, focusing on Alibaba's technologies like big data infrastructure, autonomous computing, and AI engineering for recommendation systems and digital twins.
Semantic Scholar- Tera-Scale NLP to Accelerate Scientific Research--Daniel Weld
Explore Semantic Scholar's AI-powered tools for accelerating scientific research, including adaptive feeds, personalized alerts, and advanced summarization techniques.
Deep Neural Networks for YouTube Recommendations
Explore deep neural networks for YouTube recommendations, analyzing the architecture and techniques used to enhance user experience and content discovery on the platform.
Frequently Bought Together Recommendations Using Embeddings - Challenges and Solutions
Explore embedding-based "frequently bought together" recommendations for e-commerce, covering model training, productionization, metrics, and serving strategies. Learn practical tips for large-scale implementation.
Building Recommendation Systems Using Graph Neural Networks
Explore graph neural networks for recommendation systems, focusing on RECKON's encoder-decoder architecture to improve entity intelligence and personalize user experiences.
Building Knowledge Graphs
Explore the power of knowledge graphs in data management, their applications, and strategies for successful implementation in semantic search, recommendation systems, and data fabric.
Bandits - Lecture 5
Explore multi-armed bandit algorithms and their applications in decision-making under uncertainty with Professor Kevin Jamieson from the University of Washington.
Recommendation Independence
Explore recommendation independence in AI systems, focusing on fair treatment of content providers and regularization approaches to mitigate bias in algorithmic recommendations.
Practical Defenses Against Adversarial Machine Learning
Explore real-world attacks on ML systems, from recommendation engines to email filters, and learn practical defense strategies against adversarial machine learning threats.
Check-N-Run - A Checkpointing System for Training Deep Learning Recommendation Models
Innovative checkpointing system for large ML models, reducing bandwidth and storage needs through differential checkpointing and quantization techniques without compromising training accuracy.
Recommendation Systems
Explore cutting-edge research in recommendation systems, covering algorithms, personalization techniques, and user behavior analysis for improved content suggestions.
Image Recognition with Deep Learning - From Prototype to Production
Explore image recognition using deep learning, from prototype to production. Learn to develop and implement neural networks for clothing characteristic detection, with insights on training, deployment, and front-end integration.
What Makes a Good Recommendation?
Explore the principles of effective recommendations with expert Joseph Konstan. Learn how recommendation technologies can be designed to benefit users, drawing from 24 years of research and industry insights.
Using Text Embedding Algorithms in Recommender Systems
Explore text embedding algorithms' surprising effectiveness in recommendation systems, outperforming state-of-the-art models while offering efficiency for large-scale applications.
Building a Meetup Recommendation Engine with Spring Data Neo4j
Explore building a meetup recommendation engine using Spring Data Neo4j, leveraging graph databases and Meetup.com's APIs to enhance event discovery and relevance.
Yak Shaving a Good Place to Eat Using Non Negative Matrix Factorization
Explore data handling techniques to analyze restaurant reviews, group users with similar tastes, and find personalized dining recommendations using non-negative matrix factorization.
How Booking.com Serves Deep Learning Model Predictions
Discover Booking.com's approach to deploying deep learning models in production, covering training, automated retraining, deployment, and optimization for latency and throughput.
AI in Contemporary Art
Explore AI's impact on contemporary art, from creative applications like DeepDream to critical examinations of facial recognition and deep fakes. Discover how artists and technologists are pushing boundaries and addressing societal implications.