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#Monte Carlo Tree Search

Showing: 17 courses
Stanford University

GGP Course Videos

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Comprehensive video series on General Game Playing, covering game description, management, playing strategies, and advanced topics like heuristic search and Monte Carlo methods.
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32 Lesons
3 hours 30 minutes
On-Demand
Free-Video
GERAD Research Center

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

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Explore game-theoretic reinforcement learning combining tree-search, generative models, and Nash bargaining concepts for multiagent scenarios and negotiation games.
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8 Lesons
56 minutes
On-Demand
Free-Video
Churchill CompSci Talks

How Slot Machines Are Advancing the State of the Art in Computer Go AI

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Explore Monte Carlo Tree Search in computer Go AI, covering its advantages, UCB1 strategy, and recent refinements like AMAF/RAVE, with insights into ongoing research topics.
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13 Lesons
46 minutes
On-Demand
Free-Video
Discover AI

AFLOW: Automating Agentic Workflows for Large Language Models

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Discover how AFLOW framework optimizes LLM workflows through automated code-based representations, specialized Monte Carlo Tree Search, and reusable operators to enhance AI performance while reducing computational costs.
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11 Lesons
21 minutes
On-Demand
Free-Video
Discover AI

AI Strategy Optimization Using Monte Carlo Tree Search - From Theory to Implementation

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Explore Monte Carlo Tree Search algorithms and their application in AI decision-making, from basic principles to practical implementations in healthcare and financial forecasting.
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10 Lesons
26 minutes
On-Demand
Free-Video
Trelis Research

Test Time Compute: Verifiers and Parallel Sampling - Part 2

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Explore advanced inference techniques using verifiers, parallel sampling, and prompt optimization to enhance language model performance and accuracy.
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21 Lesons
1 hour
On-Demand
Free-Video
Aleksa Gordić - The AI Epiphany

AlphaGo - Mastering the Game of Go with Deep Neural Networks and Tree Search - RL Paper Explained

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Comprehensive exploration of AlphaGo's groundbreaking AI system, detailing its neural networks, search algorithms, and innovative techniques that led to defeating professional Go players.
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14 Lesons
51 minutes
On-Demand
Free-Video
Graham Neubig

CMU Neural Nets for NLP 2017 - Advanced Search Algorithms

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Explore advanced search algorithms for NLP, including beam search and A*-type search, with a focus on their applications, benefits, and challenges in neural network-based language processing.
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11 Lesons
1 hour 2 minutes
On-Demand
Free-Video
Graham Neubig

Neural Nets for NLP 2019 - Advanced Search Algorithms

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Explore advanced search algorithms for NLP, including beam search, A* search, and search with future costs. Learn techniques to improve efficiency and effectiveness in natural language processing tasks.
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25 Lesons
59 minutes
On-Demand
Free-Video
Devoxx

From Tic Tac Toe to AlphaGo - Playing Games with AI and Machine Learning

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Explore AI's evolution from simple games to complex challenges like Go, delving into algorithms, neural networks, and breakthroughs that led to AlphaGo's triumph over human champions.
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32 Lesons
50 minutes
On-Demand
Free-Video
ACCU Conference

Total War - How AI Can Deliver Entertainment

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Explore AI techniques in Total War games, focusing on Monte Carlo Tree Search, profiling, and optimization for complex strategy environments with limited resources.
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46 Lesons
1 hour 18 minutes
On-Demand
Free-Video
MLCon | Machine Learning Conference

How do Chess Engines Work - Looking at Stockfish and AlphaZero

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Explore chess AI evolution: from Deep Blue's algorithms to Stockfish's Minimax, and AlphaZero's revolutionary self-learning approach using Monte Carlo Tree Search and neural networks.
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1 Lesons
1 hour
On-Demand
Free-Video
Chris Hay

Write Your Own AI Agent with Monte Carlo Tree Search - Building a Tic-Tac-Toe Game

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Master Monte Carlo Tree Search algorithm implementation by building a Tic-Tac-Toe AI agent, exploring its applications in modern AI systems like AlphaGo and O1 models.
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1 Lesons
32 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Modeling Individuals Without Data via Secondary Task Transfer Learning

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Discover a novel transfer learning method for modeling individual behavior using limited data from secondary tasks, outperforming standard approaches in financial health and game design.
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1 Lesons
34 minutes
On-Demand
Free-Video
Trelis Research

Improving LLM Accuracy with Monte Carlo Tree Search

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Explore Monte Carlo Tree Search to enhance LLM accuracy, focusing on boosting Llama 3 8B performance. Learn implementation, testing, and limitations through code examples and mathematical problem-solving.
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10 Lesons
33 minutes
On-Demand
Free-Video
Discover AI

Reward-Guided Tree Search for Enhanced LLM Reasoning

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Discover how reward-guided tree search enhances LLM reasoning capabilities through policy models, reward evaluation, and Monte Carlo algorithms for complex mathematical problem-solving.
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13 Lesons
25 minutes
On-Demand
Free-Video
Discover AI

Scattered Forest Search: A New Algorithm for Code Generation with LLMs

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Explore the innovative Scattered Forest Search algorithm for optimizing code generation with LLMs, covering implementation techniques, theoretical foundations, and benchmark performance.
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8 Lesons
22 minutes
On-Demand
Free-Video