Design of Recommendation Systems
Recommender systems have a wide range of applications in the industry with movie, music, and product recommendations across top tech companies like Netflix, Spotify, Amazon, etc. Consumers on the web are increasingly relying on recommendations to purchase the next product on Amazon or watc
From playlist Advanced Machine Learning
Recommender Systems - Ranking - Session 8
Importance of ranking Pointwise ranking Pairwise ranking
From playlist Recommenders Systems (Hands-on)
What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creator-spring.
From playlist Sampling
Learning model-based planning from scratch
https://arxiv.org/abs/1707.06170 Abstract: Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to constr
From playlist Reinforcement Learning
DeepMind x UCL RL Lecture Series - Planning & models [8/13]
Research Engineer Matteo Hessel explains how to learn and use models, including algorithms like Dyna and Monte-Carlo tree search (MCTS). Slides: https://dpmd.ai/planningmodels Full video lecture series: https://dpmd.ai/DeepMindxUCL21
From playlist Learning resources
Reinforcement Learning Chapter 2: Multi-Armed Bandits
Complete Book: http://incompleteideas.net/book/RLbook2018.pdf Print Version: https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262039249/ref=dp_ob_title_bk Thanks for watching this series going through the Introduction to Reinforcement Learning book! I th
From playlist Reinforcement Learning
(New Version Available) Introduction to Voting Theory and Preference Tables
Updated Version: https://youtu.be/WdtH_8lAqQo This video introduces voting theory and explains how to make a preference table from voting ballots. Site: http://mathispower4u.com
From playlist Voting Theory
Introduction to Reinforcement Learning: Chapter 1
Complete Book: http://incompleteideas.net/book/RLbook2018.pdf Print Version: https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262039249/ref=dp_ob_title_bk DeepMind Data Center Cooling: https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-cent
From playlist Reinforcement Learning
Reinforcement Learning 6: Policy Gradients and Actor Critics
Hado Van Hasselt, Research Scientist, discusses policy gradients and actor critics as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial
In this brief tutorial you're going to learn the fundamentals of deep reinforcement learning, and the basic concepts behind actor critic methods. We'll cover the Markov decision process, the agent's policy, reward discounting and why it's necessary, and the actor critic algorithm. We'll im
From playlist Get Started with Actor Critic and Policy Gradient Methods
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 12 - Fast Reinforcement Learning II
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University http://onlinehub.stanford.edu/ Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Hu
From playlist Stanford CS234: Reinforcement Learning | Winter 2019
Reinforcement Learning in the Real World | Paper Analysis
Far from being an academic novelty, reinforcement learning has many real world use cases. In this video we take a look at using reinforcement learning, specifically a version of policy gradient methods known as proximal policy optimization (PPO), to optimize the join ordering for PostgreSQ
From playlist Applications of Reinforcement Learning in the Real World
Dylan Peifer, Cornell University Title: Learning Selection Strategies in Buchberger's Algorithm Abstract: Buchberger's algorithm is the classical algorithm for computing a Gröbner basis, and highly-tuned and optimized versions are a critical part of many computer algebra systems. In prac
From playlist Fall 2019 Symbolic-Numeric Computing Seminar
Reinforcement Learning: Fundamentals II - Session 4
Goal, State Markov Decision Process (MDP) Value function Bellman equation Dynamic Programming (DP)
From playlist Reinforcement Learning
Reinforcement Learning 2: Exploration and Exploitation
Hado van Hasselt, Research scientist, further discusses the exploration and exploitation of reinforcement learning as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
Recommender Systems - What and Why of Recommender Systems - Session 2
Understanding users: wants, needs, patterns Recommend, personalize, increase engagement
From playlist Recommenders Systems (Hands-on)