Review the classification tools for ML and explore the principles and purpose of classification.
- [Instructor] Classification algorithms, … particularly neural networks, are often the first techniques … you think of when considering machine learning. … Many of the most powerful applications … of machine learning are classification systems. … Things like text recognition, speech recognition, … or facial recognition. … Neural networks based on the layered architecture … of biological brains have emerged … as a common classification technique … because they are able to group explicit, visible features … into abstract or inferred features that correlate closely … to the pre-defined classes in the training data. … Let's look at an example of how this works. … I have the exercise file open already. … If I look at the Rhino preview … I see a box shaped cloud of points. … This point cloud represents a bad point cloud scan. … Because there are points inside of the box. … If I look in Grasshopper … I see I have my bad point cloud scan importing. … But I have a bunch of training data. … This training data are lots and lots of points …
- What is generative design?
- Limitations of generative design
- Strengths and limitations of genetic/evolutional solvers
- How physics solvers work
- Testing and adjusting goals
- Working with machine learning tools
- Design requirements and diagramming
- Optimizing with Galapagos
Skill Level Intermediate
Grasshopper: Architectural Prototypingwith Walter Zesk2h 28m Intermediate
Learning Algorithmic Design with Grasshopperwith Chris Reilly3h 14m Intermediate
1. What Is Generative Design?
2. Genetic/Evolutional Solver Example
3. Physics Solver Example
4. Machine Learning Solver Example
5. Applying Generative Design
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