Andy Pack
efa7a84a8b
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json Languages/Spanish/Spanish.md STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/SLP/Perceptron Convergence.md
58 lines
1.7 KiB
Markdown
58 lines
1.7 KiB
Markdown
---
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tags:
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- ai
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---
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## Before 2010s
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- Data hungry
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- Need lots of training data
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- Processing power
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- Niche
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- No-one cared/knew about CNNs
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## After
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- [ImageNet](../CV/Datasets.md#ImageNet)
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- 16m images, 1000 classes
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- GPUs
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- General processing GPUs
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- CUDA
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- NIPS/ECCV 2012
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- Double digit % gain on [ImageNet](../CV/Datasets.md#ImageNet) accuracy
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# Full Connected
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[Dense](../MLP/MLP.md)
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- Move from [convolutional](Convolutional%20Layer.md) operations towards vector output
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- Stochastic drop-out
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- Sub-sample channels and only connect some to [dense](../MLP/MLP.md) layers
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# As a Descriptor
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- Most powerful as a deeply learned feature extractor
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- [Dense](../MLP/MLP.md) [classifier](../../Classification/Classification.md) at the end isn't fantastic
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- Use SVM to classify prior to penultimate layer #classification
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# Finetuning
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- Observations
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- Most CNNs have similar weights in [conv1](Convolutional%20Layer.md)
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- Most useful CNNs have several [conv layers](Convolutional%20Layer.md)
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- Many weights
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- Lots of training data
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- Training data is hard to get
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- Labelling
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- Reuse weights from other network
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- Freeze weights in first 3-5 [conv layers](Convolutional%20Layer.md)
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- Learning rate = 0
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- Randomly initialise remaining layers
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- Continue with existing weights
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# Training
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- Validation & training [loss](../Deep%20Learning.md#Loss%20Function)
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- Early
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- Under-fitting
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- Training not representative
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- Later
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- Overfitting
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- V.[loss](../Deep%20Learning.md#Loss%20Function) can help adjust learning rate
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- Or indicate when to stop training
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