This course is about making Machine Learning the next tool in your Houdini toolbox. Just like some problems are best solved with vector maths or Vex or Python, some problems get way easier with ML and we want to be able to spot these problems […]
Machine Learning 101
About This Course
This course is about making Machine Learning the next tool in your Houdini toolbox. Just like some problems are best solved with vector maths or Vex or Python, some problems get way easier with ML and we want to be able to spot these problems and solve them using your own custom networks.
To do so we create our own training data in Houdini, we build and train our own neural nets with Python and PyTorch and finally integrate them into our Houdini Workflows using ONNX. Additionally we want to take really deep looks into how these setups work and why they’re built the way they are to get a ton of intuition for applying these techniques to new problems.
Recommended Prerequisites
- General Houdini UI/UX knowledge
- Experience in Creative Coding
- Basic Vector Math
ML101 – pt. 01: DIY Neural Net & Regression Basics
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ML101 – pt. 02: The Python Install Episode
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ML101 – pt. 03: Transferring training data from Houdini to Python
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ML101 – pt. 04: From Python back to Houdini
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ML101 – pt. 05: Adding Tensorboard
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ML101 – pt. 06: Finishing our Neural Net
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ML101 – pt. 07: Section Overview and Pose Generation
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ML101 – pt. 08: Pose Prep 01 – Extracting Differences
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ML101 – pt. 09: Reducing our Data – the PCA Node
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ML101 – pt. 10: Reducing our Data – Encoding, Exporting and Training our Data-Set
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ML101 – pt. 11: Building the Inference Setup
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ML101 – pt. 12: Addendum A: Stacking Additional Models
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ML101 – pt. 13: Generating Poses With Vellum And PDG
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