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Questions and Answers
What are the inputs to the neuron in the model of an artificial neuron?
What are the inputs to the neuron in the model of an artificial neuron?
X1, X2, ..., Xn
What are the real-valued parameters in the model of an artificial neuron called?
What are the real-valued parameters in the model of an artificial neuron called?
Weights
What is the formula for calculating the weighted sum in the model of an artificial neuron?
What is the formula for calculating the weighted sum in the model of an artificial neuron?
net = W1 * X1 + W2 * X2 + ... + Wn * Xn
In the model of an artificial neuron, what is f called?
In the model of an artificial neuron, what is f called?
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What is the output of the neuron in the model of an artificial neuron?
What is the output of the neuron in the model of an artificial neuron?
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A single-layer network is also known as a multi-layer network.
A single-layer network is also known as a multi-layer network.
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What are the layers between the input and output layer in a multi-layer network called?
What are the layers between the input and output layer in a multi-layer network called?
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Which type of machine learning involves training a machine with labeled data?
Which type of machine learning involves training a machine with labeled data?
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Which type of machine learning involves training a machine with unlabeled data?
Which type of machine learning involves training a machine with unlabeled data?
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Unsupervised learning is computationally less complex and more accurate than supervised learning.
Unsupervised learning is computationally less complex and more accurate than supervised learning.
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Study Notes
Artificial Intelligence
- Artificial intelligence is a rapidly growing field.
- Artificial neurons are models of biological neurons.
Model of an Artificial Neuron
- Inputs (x₁, x₂, ..., xₙ) are fed into the node.
- Weights (w₁, w₂, ..., wₙ) are real-valued parameters.
- The weighted sum (net) is calculated: net = w₁x₁ + w₂x₂ + ... + wₙxₙ
- An activation function (f) processes the weighted sum.
- Output (y) is determined by f(net).
Network Architecture
- Single layer net: A simple network with one layer of neurons between the input and output layers. Input nodes feed into output layer.
- Multi-layer net: A network with one or more hidden layers between the input and output layers. Hidden layers process the information from the input layer, passing it to the output layer.
Online Learning
- Healthy subjects have brain networks.
- SSAE prototype and Transfer Learning used in this training model
- Prior knowledge aided training is an important component of online learning
- Softmax regression and DLN-NN are models used in classification of data.
Types of Machine Learning
- Supervised Learning: Training involves labeled data.
- Unsupervised Learning: Training uses unlabeled data.
Supervised Learning
- Description: The machine learns from labeled data.
- Using labeled fruit examples, the algorithm learns to correctly classify other objects.
- The machine uses labeled training data to create a predictive model
- Training data is crucial for building a model
Unsupervised Learning
- Description: The machine learns from unlabeled data.
- Example: Recognizing patterns and grouping similar items together without explicit labels.
- This machine learning model does not rely on labeled data to predict the output.
Supervised and Unsupervised Learning Processes
- Supervised: There is a labelled dataset (in the training phase). Data is processed to develop a predictive model. A feedback loop refines the model.
- Unsupervised: Model learns from unlabeled data, determining patterns, groupings, or relationships without prior, or explicit, instructions.
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Description
Explore the fundamentals of artificial intelligence, including neural networks and their architectures. Learn about artificial neurons, online learning, and the key concepts that drive this rapidly evolving field.
