Bayesian Deep Learning For Manufacturing (dlmfg)
2.0.0
  • Installation and Implementation
  • Real System Implementation (3D Optical Scanner: WLS400A)
  • Library Configuration
  • Data Description & Processing
  • Model Architecture
  • Deep Reinforcement Learning for Control and Correction
  • Model Architecture Selection
  • Bayesian Deep Learning
  • 3D U-Net Architecture Global Interpretability
  • 3D U-Net Architecture Local Interpretability
  • 3D U-Net Segmentation
  • Class Structure: Objects and Methods
  • Key Measurement Characteristics (KMCs)
  • Utilities
  • Continual and Transfer Learning
  • Active Learning/Adaptive Sampling
  • Visualization
  • Case Study: Positioning and Clamping Variations for Halo
  • Case Study: Door Inner and Hinge Reinforcement Multi-Stage Assembly
  • Case Study: Cross Member Multi-Station Assembly
  • Research and References
Bayesian Deep Learning For Manufacturing (dlmfg)
  • Docs »
  • Overview: module code

All modules for which code is available

  • dlmfg.active_learning.sampling_system
  • dlmfg.core.assembly_system
  • dlmfg.core.core_model
  • dlmfg.core.data_download
  • dlmfg.core.data_import
  • dlmfg.core.measurement_system
  • dlmfg.core.metrics_eval
  • dlmfg.core.model_deployment
  • dlmfg.core.model_train
  • dlmfg.core.wls400a_system
  • dlmfg.kmc_gen.kmc_model
  • dlmfg.transfer_learning.tl_core
  • dlmfg.utilities.benchmarking
  • dlmfg.utilities.voxel_construction
  • dlmfg.visualization.cop_viz
  • dlmfg.visualization.training_viz

© Copyright 2019, Sumit Sinha

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