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Affected files: Money/Assets/Financial Instruments.md Money/Assets/Security.md Money/Markets/Markets.md Politcs/Now.md STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/FlowNet.md STEM/AI/Neural Networks/CNN/FCN/Highway Networks.md STEM/AI/Neural Networks/CNN/FCN/ResNet.md STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md STEM/AI/Neural Networks/CNN/FCN/Super-Resolution.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/CNN/GAN/StackGAN.md STEM/AI/Neural Networks/CNN/Inception Layer.md STEM/AI/Neural Networks/CNN/Interpretation.md STEM/AI/Neural Networks/CNN/Max Pooling.md STEM/AI/Neural Networks/CNN/Normalisation.md STEM/AI/Neural Networks/CNN/UpConv.md STEM/AI/Neural Networks/CV/Layer Structure.md STEM/AI/Neural Networks/MLP/MLP.md STEM/AI/Neural Networks/Neural Networks.md STEM/AI/Neural Networks/RNN/LSTM.md STEM/AI/Neural Networks/RNN/RNN.md STEM/AI/Neural Networks/RNN/VQA.md STEM/AI/Neural Networks/SLP/Least Mean Square.md STEM/AI/Neural Networks/SLP/Perceptron Convergence.md STEM/AI/Neural Networks/SLP/SLP.md STEM/AI/Neural Networks/Transformers/LLM.md STEM/AI/Neural Networks/Transformers/Transformers.md STEM/AI/Properties.md STEM/CS/Language Binding.md STEM/Light.md STEM/Maths/Tensor.md STEM/Quantum/Orbitals.md STEM/Quantum/Schrödinger.md STEM/Quantum/Standard Model.md STEM/Quantum/Wave Function.md Tattoo/Music.md Tattoo/Plans.md Tattoo/Sources.md
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@ -28,7 +28,7 @@
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# GoogLeNet
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2015
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- [[Inception Layer]]s
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- [Inception Layer](Inception%20Layer.md)s
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- Multiple [[Deep Learning#Loss Function|Loss]] Functions
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@ -1,4 +1,4 @@
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Fully [[Convolution]]al Network
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Fully [Convolution](../../../../Signal%20Proc/Convolution.md)al Network
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[[Convolutional Layer|Convolutional]] and [[UpConv|up-convolutional layers]] with [[Activation Functions#ReLu|ReLu]] but no others (pooling)
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- All some sort of Encoder-Decoder
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@ -9,12 +9,11 @@ Contractive → [UpConv](../UpConv.md)
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- For visual output
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- Previously image $\rightarrow$ vector
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- Additional layers to up-sample representation to an image
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- Up-[[convolution]]al
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- De-[[convolution]]al
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- Up-[convolution](../../../../Signal%20Proc/Convolution.md)al
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- De-[convolution](../../../../Signal%20Proc/Convolution.md)al
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![[fcn-uses.png]]
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![[fcn-arch.png]]
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# Training
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- Rarely from scratch
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@ -22,7 +21,7 @@ Contractive → [UpConv](../UpConv.md)
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- Replace final layers
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- [[MLP|FC]] layers
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- White-noise initialised
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- Add [[upconv]] layer(s)
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- Add [UpConv](../UpConv.md) layer(s)
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- Fine-tune train
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- Freeze others
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- Annotated GT images
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@ -7,16 +7,16 @@ Optical Flow
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# [[Skip Connections]]
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# [Skip Connections](Skip%20Connections.md)
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- Further through the network information is condensed
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- Less high frequency information
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- Link encoder layers to [[upconv]] layers
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- Link encoder layers to [UpConv](../UpConv.md) layers
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- Append activation maps from encoder to decoder
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# Encode
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# [[Upconv]]
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# [UpConv](../UpConv.md)
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# Training
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@ -1,9 +1,9 @@
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- [[Skip connections]] across individual layers
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- [Skip Connections](Skip%20Connections.md) across individual layers
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- Conditionally
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- Soft gates
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- Learn vs carry
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- Gradients propagate further
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- Inspired by [[LSTM]] [[RNN]]s
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- Inspired by [LSTM](../../RNN/LSTM.md) [RNN](../../RNN/RNN.md)s
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![[highway-vs-residual.png]]
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![[skip-connections 1.png]]
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@ -24,7 +24,7 @@
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- No dropout
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[[Datasets#ImageNet|ImageNet]] Error:
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![[imagenet-error.png]]
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![[resnet-arch.png]]
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![[resnet-arch2.png]]
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@ -1,16 +1,16 @@
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- Output of [[Convolutional Layer|conv]], c, layers are added to inputs of [[upconv]], d, layers
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- Output of [[Convolutional Layer|conv]], c, layers are added to inputs of [UpConv](../UpConv.md), d, layers
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- Element-wise, not channel appending
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- Propagate high frequency information to later layers
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- Two types
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- Additive
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- [[ResNet]]
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- [[Super-resolution]] auto-encoder
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- [ResNet](ResNet.md)
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- [Super-Resolution](Super-Resolution.md) auto-encoder
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- Concatenative
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- Densely connected architectures
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- DenseNet
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- [[FlowNet]]
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- [FlowNet](FlowNet.md)
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![[STEM/img/skip-connections.png]]
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[AI Summer - Skip Connections](https://theaisummer.com/skip-connections/)
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[Arxiv - Visualising the Loss Landscape](https://arxiv.org/abs/1712.09913)
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@ -7,6 +7,6 @@
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- Unsupervised?
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- Decoder stage
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- Identical architecture to encoder
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![[super-res.png]]
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- Is actually contractive/up sampling
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![[superres-results.png]]
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@ -12,8 +12,8 @@ Deep [Convolutional](../../../../Signal%20Proc/Convolution.md) [GAN](GAN.md)
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- Train using Gaussian random noise for code
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- Discriminator
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- Contractive
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- Cross-entropy [[Deep Learning#Loss Function|loss]]
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- [[Convolutional Layer|Conv]] and leaky [[Activation Functions#ReLu|ReLu]] layers only
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- Cross-entropy [loss](../../Deep%20Learning.md#Loss%20Function)
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- [Conv](../Convolutional%20Layer.md) and leaky [[Activation Functions#ReLu|ReLu]] layers only
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- Normalised output via [[Activation Functions#Sigmoid|sigmoid]]
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## [[Deep Learning#Loss Function|Loss]]
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@ -1,6 +1,6 @@
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# Fully [[Convolution]]al
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- Remove [[Max Pooling]]
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- Use strided [[upconv]]
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# Fully [Convolution](../../../../Signal%20Proc/Convolution.md)al
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- Remove [Max Pooling](../Max%20Pooling.md)
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- Use strided [UpConv](../UpConv.md)
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- Remove [[MLP|FC]] layers
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- Hurts convergence in non-classification
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- Normalisation tricks
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@ -16,16 +16,16 @@
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- Discriminator is a classifier
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- Is image fake or real
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![[gan-arch.png]]
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![[gan-arch2.png]]
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![[gan-results.png]]
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]
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# Training
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![[gan-training-discriminator.png]]
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![[gan-training-generator.png]]
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# Code Vector Math for Control
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![[cvmfc.png]]
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- Do [[Interpretation#Activation Maximisation|AM]] to derive code for an image
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![[code-vector-math-for-control-results.png]]
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@ -1,6 +1,6 @@
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- Feed output from synthesis into up-res network
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- Generate standard low-res image
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- Feed into [[cGAN]]
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- Feed into [cGAN](cGAN.md)
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![[stackgan.png]]
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![[stackgan-results.png]]
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- Couple of different scales
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- Concatenate results
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![[inception-layer-effect.png]]
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![[inception-layer-arch.png]]
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- 1 x 1
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- Averages over channels
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- Maximise 1-hot output
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- Maximise [[Activation Functions#SoftMax|SoftMax]]
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![[am.png]]
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- **Use trained network**
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- Don't update weights
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- [[Architectures|Feedforward]] noise
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- Don't update weights
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- Update image
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![[am-process.png]]
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## Regulariser
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- Fit to natural image statistics
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- Prone to high frequency noise
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@ -24,7 +24,7 @@ $$x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_
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$$x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_0)+\lambda\mathcal R(x)$$
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- Need a regulariser like above
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![[am-regulariser.png]]
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$$\mathcal R_{V^\beta}(f)=\int_\Omega\left(\left(\frac{\partial f}{\partial u}(u,v)\right)^2+\left(\frac{\partial f}{\partial v}(u,v)\right)^2\right)^{\frac \beta 2}du\space dv$$
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- Max value is the good bit
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- No parameters
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![[max-pooling.png]]
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## Design Parameters
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- Size of input image
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@ -2,4 +2,4 @@
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- Apply kernel to same location of all channels
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- Pixels in window divided by sum of pixel within volume across channels
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![[cnn-normalisation.png]]
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- Fractionally strided convolution
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- Transposed [[convolution]]
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- Transposed [Convolution](../../../Signal%20Proc/Convolution.md)
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- Like a deep interpolation
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- Convolution with a fractional input stride
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- Up-sampling is convolution 'in reverse'
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- Not an actual inverse convolution
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- For scaling up by a factor of $f$
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- Consider as a [[convolution]] of stride $1/f$
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- Consider as a [Convolution](../../../Signal%20Proc/Convolution.md) of stride $1/f$
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- Could specify kernel
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- Or learn
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- Can have multiple upconv layers
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- For non-linear up-sampling conv
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- Interpolation is linear
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![[upconv.png]]
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# Convolution Matrix
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Normal
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![[upconv-matrix.png]]
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- Equivalent operation with a flattened input
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- Row per kernel location
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- Many-to-one operation
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![[upconv-matrix-result.png]]
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[Understanding transposed convolutions](https://www.machinecurve.com/index.php/2019/09/29/understanding-transposed-convolutions/)
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## Transposed
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![[upconv-transposed-matrix.png]]
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- One-to-many
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![[upconv-matrix-transposed-result.png]]
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@ -1 +1 @@
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![[cnn-cv-layer-arch.png]]
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@ -3,20 +3,20 @@
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- Universal approximation theorem
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- Each hidden layer can operate as a different feature extraction layer
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- Lots of [[Weight Init|weights]] to learn
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- [[Back-Propagation]] is supervised
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- [Back-Propagation](Back-Propagation.md) is supervised
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![[mlp-arch.png]]
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# Universal Approximation Theory
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A finite [[Architectures|feedforward]] MLP with 1 hidden layer can in theory approximate any mathematical function
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- In practice not trainable with [[Back-Propagation|BP]]
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![[activation-function.png]]
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![[mlp-arch-diagram.png]]
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## Weight Matrix
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- Use matrix multiplication for layer output
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- TLU is hard limiter
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![[tlu.png]]
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- $o_1$ to $o_4$ must all be one to overcome -3.5 bias and force output to 1
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![[mlp-non-linear-decision.png]]
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- Can generate a non-linear [[Decision Boundary|decision boundary]]
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- Interneuron connection strengths store acquired knowledge
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- Synaptic weights
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![[slp-arch.png]]
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A neural network is a directed graph consisting of nodes with interconnecting synaptic and activation links, and is characterised by four properties
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Long Short Term Memory
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- More general form of [[RNN]]
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- More general form of [RNN](RNN.md)
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- Explicitly encode memory state, C
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![[lstm.png]]
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![[lstm-slp.png]]
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@ -13,5 +13,5 @@ Recurrent Neural Network
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- In practice suffers from vanishing gradient
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- Can't extract precise information about previous tokens
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![[rnn-input.png]]
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![[rnn-recurrence.png]]
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Visual Question Answering
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- Combine visual with text sequence
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- [[CNN]] + [[LSTM]]
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- [CNN](../CNN/CNN.md) + [LSTM](LSTM.md)
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- Generate text from images
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- Automatic scene description
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- Cross-modal
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![[cnn+lstm.png]]
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- Word embedding not character
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# Freeform
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- Encode facts with two text streams
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![[vqa-block.png]]
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# Limitations
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- Repetitive answers
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- Not much variation
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@ -59,16 +59,16 @@ $$\hat{w}(n+1)=\hat{w}(n)+\eta \cdot x(n) \cdot e(n)$$
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- Sensitivity to variation in eigenstructure of input
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- Typically requires iterations of 10 x dimensionality of the input space
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- Worse with high-d input spaces
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![[slp-mse.png]]
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- Use steepest descent
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- Partial derivatives
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![[slp-steepest-descent.png]]
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- Can be solved by matrix inversion
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- Stochastic
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- Random progress
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- Will overall improve
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![[lms-algorithm.png]]
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$$\hat{w}(n+1)=\hat{w}(n)+\eta\cdot x(n)\cdot[d(n)-x^T(n)\cdot\hat w(n)]$$
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$$=[I-\eta\cdot x(n)x^T(n)]\cdot\hat{w}(n)+\eta\cdot x(n)\cdot d(n)$$
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@ -76,6 +76,6 @@ $$=[I-\eta\cdot x(n)x^T(n)]\cdot\hat{w}(n)+\eta\cdot x(n)\cdot d(n)$$
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Where
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$$\hat w(n)=z^{-1}[\hat w(n+1)]$$
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## Independence Theory
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![[slp-lms-independence.png]]
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![[sl-lms-summary.png]]
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@ -39,4 +39,4 @@ $$
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2. Fast adaptation with respect to real changes in the underlying distribution of process responsible for $x$
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- Large eta
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![[slp-separable.png]]
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@ -1,7 +1,7 @@
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![[slp-arch.png]]
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$$v(n)=\sum_{i=0}^{m}w_i(n)x_i(n)$$
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$$=w^T(n)x(n)$$
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![[slp-hyperplane.png]]
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Perceptron learning is performed for a finite number of iteration and then stops
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[[Least Mean Square|LMS]] is continuous learning that doesn't stop
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@ -8,9 +8,9 @@
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## Hallucination
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# Architectures
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Mostly [[Transformers]]
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Mostly [Transformers](Transformers.md)
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## GPT
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Generative Pre-trained [[Transformers]]
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Generative Pre-trained [Transformers](Transformers.md)
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|
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![[llm-family-tree.png]]
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@ -1,15 +1,15 @@
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- [[Attention|Self-attention]]
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- Weighting significance of parts of the input
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- Including recursive output
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- Similar to [[RNN]]s
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- Similar to [RNN](../RNN/RNN.md)s
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- Process sequential data
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- Translation & text summarisation
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- Differences
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- Process input all at once
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- Largely replaced [[LSTM]] and gated recurrent units (GRU) which had attention mechanics
|
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- Largely replaced [LSTM](../RNN/LSTM.md) and gated recurrent units (GRU) which had attention mechanics
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- No recurrent structure
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![[transformer-arch.png]]
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## Examples
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- BERT
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@ -34,6 +34,6 @@
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- Takes encodings and does opposite
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- Uses incorporated textual information to produce output
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- Has attention to draw information from output of previous decoders before drawing from encoders
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- Both use [[attention]]
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- Both use [Attention](Attention.md)
|
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- Both use [[MLP|dense]] layers for additional processing of outputs
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- Contain residual connections & layer norm steps
|
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@ -16,7 +16,7 @@ An AI system must be able to
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2. Apply knowledge to solve problems
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3. Acquire new knowledge through experience
|
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|
||||
![[ai-nested-subjects.png]]
|
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# Expert Systems
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- Usually easier to obtain compiled experience from experts than duplicate experience that made them experts for network
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@ -56,4 +56,4 @@ Symbolic AI is the formal manipulation of a language of algorithms and data repr
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Neural nets bottom-up
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|
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![[ai-io.png]]
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|
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@ -5,18 +5,18 @@
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### Object Models
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- COM
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- [[C++]]
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- [C++](Languages/C++.md)
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- Component Object Model
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- MS only cross-language model
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- CLI
|
||||
- [[dotNet]]
|
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- [dotNet](Languages/dotNet.md)
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- .NET Common Language Infrastructure
|
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- Freedesktop.org D-Bus
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- Open cross-platform-language model
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||||
|
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### Virtual Machines
|
||||
- CLR
|
||||
- [[dotNet]]
|
||||
- [dotNet](Languages/dotNet.md)
|
||||
- .NET Common Language Runtime
|
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- Mono
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- CLI languages
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6
Light.md
6
Light.md
@ -9,9 +9,9 @@ $$E=hf$$
|
||||
2. There is a minimum frequency n0below which there is no emission
|
||||
3. No time delay (less than 1 ns) before the onset of emission –but the rate of electrons depends on the intensity.
|
||||
|
||||
![[photo-electric.png]]
|
||||
![[fermi-vacuum-level.png]]
|
||||
|
||||
|
||||
|
||||
![[em-spectrum.png]]
|
||||
|
||||
- Radio spectrum
|
||||
- 30Hz – 300 GHz
|
||||
@ -11,7 +11,7 @@
|
||||
- Cube matrix
|
||||
|
||||
Matrices are not inherently rank-2 tensors. Matrices are just the formatting structure. The tensor described by the matrix must follow the transformation rules to be a tensor
|
||||
![[tensor.png]]
|
||||
|
||||
# Transformation Rules
|
||||
|
||||
1. Transforms like a tensor
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
[[Wave Function]]
|
||||
[Wave Function](Wave%20Function.md)
|
||||
|
||||
## Quantum Numbers
|
||||
$$n$$
|
||||
@ -17,7 +17,7 @@ Z-component / Magentic of $l$
|
||||
- $-l$ to $+l$
|
||||
- ***Orientation*** of orbital
|
||||
|
||||
![[wave-function-polar-segment.png]]
|
||||
|
||||
|
||||
## Filling
|
||||
|
||||
@ -30,11 +30,11 @@ Z-component / Magentic of $l$
|
||||
- Orbitals with same energy filled one at a time
|
||||
- Degenerate
|
||||
|
||||
![[orbitals-radius.png]]
|
||||
![[wave-function-nodes.png]]
|
||||
|
||||
|
||||
|
||||
## Radial
|
||||
![[radial-equations.png]]
|
||||
|
||||
|
||||
- Z = Atomic number
|
||||
- Bohr radius
|
||||
@ -42,4 +42,4 @@ Z-component / Magentic of $l$
|
||||
- Normalisation
|
||||
- $\int_0^\infty r^2R_{nl}^*R_{nl}dr=1$
|
||||
|
||||
![[radius-electron-density-wf.png]]
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
$$-\frac{\hbar^2}{2m}\nabla^2\psi+V\psi=E\psi$$
|
||||
- Time Independent
|
||||
- $\psi$ is the [[Wave Function]]
|
||||
- $\psi$ is the [Wave Function](Wave%20Function.md)
|
||||
|
||||
Quantum counterpart of Newton's second law in classical mechanics
|
||||
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
![[model-table.png]]
|
||||
|
||||
- 4 fundamental forces
|
||||
- Bosons
|
||||
- Elementary particles
|
||||
@ -22,5 +22,5 @@
|
||||
- Force carriers
|
||||
- y, W, Z, g
|
||||
|
||||
![[boson-interactions-feynman.png]]
|
||||
![[boson-interactions.png]]
|
||||
|
||||
|
||||
@ -5,17 +5,17 @@ Radial Function
|
||||
Spherical Harmonic
|
||||
- $Y_{ml}(\theta, \phi)$
|
||||
|
||||
Forms [[Orbitals]]
|
||||
Forms [Orbitals](Orbitals.md)
|
||||
|
||||
Absolute value of wave function squared gives probability density of finding electron inside differential volume $dV$ centred on $r, \theta, \phi$
|
||||
|
||||
$$|\psi(r,\theta,\phi)|^2$$
|
||||
![[wave-function-polar.png]]
|
||||
|
||||
|
||||
![[hydrogen-wave-function.png]]
|
||||
![[wave-function-polar-segment.png]]
|
||||
![[wave-function-nodes.png]]
|
||||
|
||||
|
||||
|
||||
|
||||
![[hydrogen-electron-density.png]]
|
||||
|
||||
|
||||
![[radius-electron-density-wf.png]]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user