andy
b30da1d29c
Affected files: .obsidian/graph.json .obsidian/workspace.json Money/Assets/Derivative.md STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/MLP/Decision Boundary.md STEM/CS/Languages/dotNet.md STEM/Semiconductors/Equations.md Tattoo/Engineering.md
47 lines
1.9 KiB
Markdown
47 lines
1.9 KiB
Markdown
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# Loss Function
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Objective Function
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- [Back-Propagation](MLP/Back-Propagation.md)
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- Difference between predicted and target outputs
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- Test accuracy worse than train accuracy = overfitting
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- [[MLP|Dense]] = [[MLP|fully connected]]
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- Automates feature engineering
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These are the two essential characteristics of how deep learning learns from data: the incremental, layer-by-layer way in which increasingly complex representations are developed, and the fact that these intermediate incremental representations are learned jointly, each layer being updated to follow both the representational needs of the layer above and the needs of the layer below. Together, these two properties have made deep learning vastly more successful than previous approaches to machine learning.
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# Steps
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Structure defining
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Compilation
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- Loss function
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- Metric of difference between output and target
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- Optimiser
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- How network will update
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- Metrics to monitor
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- Testing and training
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Data preprocess
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- Reshape input frame into linear array
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- Categorically encode labels
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Fit
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Predict
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Evaluate
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# Data Structure
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- [Tensor](../../Maths/Tensor.md) flow = channels last
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- (samples, height, width, channels)
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- Vector data
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- 2D [tensors](../../Maths/Tensor.md) of shape (samples, features)
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- Time series data or sequence data
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- 3D [tensors](../../Maths/Tensor.md) of shape (samples, timesteps, features)
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- Images
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- 4D [tensors](../../Maths/Tensor.md) of shape (samples, height, width, channels) or (samples, channels, height, Width)
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- Video
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- 5D [tensors](../../Maths/Tensor.md) of shape (samples, frames, height, width, channels) or (samples, frames, channels , height, width)
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