2 repository-uri
Awesome GitHub RepositoriesDistributed Learning Algorithms
Machine learning algorithms specifically designed to be executed across a distributed cluster of compute nodes.
Distinct from Machine Learning Algorithms: The candidates are either too specific (Gradient Boosting) or unrelated (consensus/storage), and this requires a general category for distributed ML algorithms.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Distributed Learning Algorithms. Refine with filters or upvote what's useful.
Awesome Distributed Learning Algorithms GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- h2oai/h2o-3
h2oai/h2o-3
7,493Vezi pe GitHubh2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i
Supports the training of predictive models using distributed versions of algorithms like gradient boosting and deep learning.
Jupyter Notebookautomlbig-datadata-science - numenta/nupic
numenta/nupic
6,352Vezi pe GitHubNuPIC is a machine learning framework that implements Hierarchical Temporal Memory (HTM) theory, a neuroscience-inspired approach to artificial intelligence. It models principles of the neocortex to build systems capable of learning patterns from streaming data, performing sequence prediction, and detecting anomalies in real-time data streams. The framework is built around a Cortical Learning Algorithm that combines spatial pooling and temporal memory to process streaming input. It uses Sparse Distributed Representations to encode input patterns, a Spatial Pooler to convert dense input into s
Combines spatial pooling and temporal memory to learn and infer patterns from streaming data, mimicking neocortical processing.
Python
