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Affected files: STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Gradient Boosting Machine.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised.md STEM/AI/Classification/Supervised/README.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Learning.md STEM/AI/Neural Networks/Learning/Boltzmann.md STEM/AI/Neural Networks/Learning/Competitive Learning.md STEM/AI/Neural Networks/Learning/Credit-Assignment Problem.md STEM/AI/Neural Networks/Learning/Hebbian.md STEM/AI/Neural Networks/Learning/Learning.md STEM/AI/Neural Networks/Learning/README.md STEM/AI/Neural Networks/RNN/Autoencoder.md STEM/AI/Neural Networks/RNN/Deep Image Prior.md STEM/AI/Neural Networks/RNN/MoCo.md STEM/AI/Neural Networks/RNN/Representation Learning.md STEM/AI/Neural Networks/RNN/SimCLR.md STEM/img/comp-learning.png STEM/img/competitive-geometric.png STEM/img/confusion-matrix.png STEM/img/decision-tree.png STEM/img/deep-image-prior-arch.png STEM/img/deep-image-prior-results.png STEM/img/hebb-learning.png STEM/img/moco.png STEM/img/receiver-operator-curve.png STEM/img/reinforcement-learning.png STEM/img/rnn+autoencoder-variational.png STEM/img/rnn+autoencoder.png STEM/img/simclr.png STEM/img/sup-representation-learning.png STEM/img/svm-c.png STEM/img/svm-non-linear-project.png STEM/img/svm-non-linear-separated.png STEM/img/svm-non-linear.png STEM/img/svm-optimal-plane.png STEM/img/svm.png STEM/img/unsup-representation-learning.png |
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| Learning | ||
| MLP | ||
| RNN | ||
| SLP | ||
| Transformers | ||
| Activation Functions.md | ||
| Architectures.md | ||
| Deep Learning.md | ||
| Neural Networks.md | ||
| Properties+Capabilities.md | ||
| README.md | ||
| Training.md | ||
| Weight Init.md | ||
- Massively parallel, distributed processor
- Natural propensity for storing experiential knowledge
Resembles Brain
- Knowledge acquired from by network through learning
- Interneuron connection strengths store acquired knowledge
- Synaptic weights
A neural network is a directed graph consisting of nodes with interconnecting synaptic and activation links, and is characterised by four properties
- Each neuron is represented by a set of linear synaptic links, an externally applied bias, and a possibly nonlinear activation link. The bias is represented by a synaptic link connected to an input fixed at +1
- The synaptic links of a neuron weight their respective input signals
- The weighted sum of the input signals defines the induced local field of the neuron in question
- The activation link squashes the induced local field of the neuron to produce an output
Knowledge
Knowledge refers to stored information or models used by a person or machine to interpret, predict, and appropriately respond to the outside world
Made up of:
- The known world state
- Represented by facts about what is and what has been known
- Prior information
- Observations of the world
- Usually inherently noisy
- Measurement error
- Pool of information used to train
- Can be labelled or not
- (Un-)Supervised
Knowledge representation of the surrounding environment is defined by the values taken on by the free parameters of the network
- Synaptic weights and biases