2 repository-uri
Awesome GitHub RepositoriesIterative Local Optimization
Techniques that refine candidate solutions through repeated small changes to optimize a value function.
Distinct from Iterative Error Minimization: Focuses on AI-specific local search and iterative improvement rather than numerical error minimization or image optimization.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Iterative Local Optimization. Refine with filters or upvote what's useful.
Awesome Iterative Local Optimization GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- aimacode/aima-python
aimacode/aima-python
8,675Vezi pe GitHubThis project is a Python collection of algorithms and data structures that implement the concepts from the Artificial Intelligence: A Modern Approach textbook. It serves as an educational resource for learning core artificial intelligence concepts through the implementation of classic algorithms for searching, logic, and problem solving. The repository functions as an automated reasoning toolset for managing knowledge bases, a game theory engine for calculating optimal moves in competitive games, and a search and optimization library. It provides specialized frameworks for deriving logical co
Refines candidate solutions through repeated small changes to maximize value functions or minimize error.
Jupyter Notebook - tensorflow/minigo
tensorflow/minigo
3,531Vezi pe GitHubMinigo is a TensorFlow-based reinforcement learning engine designed to master the game of Go. It functions as a comprehensive system for training neural networks to predict board policies and game outcomes, utilizing a model trainer to generate self-play data and optimize weights. The project is distinguished by its ability to perform large-scale game simulations using Kubernetes to distribute worker nodes across CPU, GPU, and TPU hardware. It employs a Monte Carlo Tree Search implementation to identify optimal moves and supports specialized hardware acceleration, including inference on Edge
Refines playing strategy through iterative optimization using learning rate schedules and batch normalization.
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