Flux ML

GPTKB entity

Statements (66)
Predicate Object
gptkbp:instance_of gptkb:software_framework
gptkbp:bfsLayer 5
gptkbp:bfsParent gptkb:Flux.jl
gptkbp:application gptkb:microprocessor
Optimization Problems
Probabilistic Programming
gptkbp:community Active Contributors
gptkbp:conference gptkb:Julia_Con
Machine Learning Conferences
gptkbp:developed_by gptkb:Julia_Computing
gptkbp:features Differentiable Programming
GPU Support
Composable Layers
gptkbp:has_documentation Comprehensive Guides
https://www.w3.org/2000/01/rdf-schema#label Flux ML
gptkbp:integrates_with Julia Ecosystem
gptkbp:is_compared_to gptkb:Graphics_Processing_Unit
gptkb:Keras
gptkb:Py_Torch
gptkbp:language gptkb:Julia
gptkbp:license MIT License
gptkbp:notable_feature gptkb:software
Research Collaborations
Community Contributions
Interoperability with Other Languages
Real-time Data Processing
Visualization Tools
Dynamic Computation Graphs
Pre-trained Models
Model Serialization
Extensible Architecture
User-friendly API
Support for Graph Neural Networks
Support for Reinforcement Learning
Support for Transfer Learning
Support for Natural Language Processing
Support for Custom Loss Functions
Support for Custom Optimizers
Support for Explainable AI
Support for Federated Learning
Support for Time Series Analysis
Support for Image Processing
Custom Gradients
Ecosystem Compatibility
Support for Bayesian Methods
Support for Custom Layers
Support for Ensemble Methods
Support for Meta-learning
Support for Multi-task Learning
Type Stability
gptkbp:performance High Performance
gptkbp:release_date gptkb:2018
gptkbp:repository gptkb:archive
gptkbp:supports gptkb:software_framework
Multi-threading
Automatic Differentiation
gptkbp:tutorials Example Projects
Advanced Tutorials
Beginner Tutorials
gptkbp:type gptkb:project
gptkbp:use_case gptkb:Educational_Institution
gptkb:Research_Institute
Industry Applications
gptkbp:user_base gptkb:physicist
gptkb:software
Data Scientists