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Questions and Answers
What is the primary objective of adjusting weights in a feed-forward neural network?
What is the primary objective of adjusting weights in a feed-forward neural network?
- To increase the number of input units
- To reduce the number of hidden units
- To change the function represented by the network (correct)
- To implement sigmoid activation functions
What is the alternate name of the Multilayer Perceptron model?
What is the alternate name of the Multilayer Perceptron model?
- Backpropagation algorithm (correct)
- Deep Learning Architecture
- Feed-forward Neural Network
- Single Layer Perceptron
What is the derivative of the sigmoid function g(x) = 1/(1 + e^(-x))?
What is the derivative of the sigmoid function g(x) = 1/(1 + e^(-x))?
- g(x) / (1 - g(x))
- g(x) ∗(g(x) - 1)
- g(x) / (g(x) - 1)
- g(x) ∗(1 - g(x)) (correct)
What is a characteristic of hidden units in a Multilayer Perceptron?
What is a characteristic of hidden units in a Multilayer Perceptron?
What is a requirement for deep learning algorithms to solve complicated issues?
What is a requirement for deep learning algorithms to solve complicated issues?
What happens when the bias weight W0,i is changed in a neural network?
What happens when the bias weight W0,i is changed in a neural network?
What is a common implementation of the activation function in a Multilayer Perceptron?
What is a common implementation of the activation function in a Multilayer Perceptron?
What is the simplest type of artificial neural network?
What is the simplest type of artificial neural network?
What can a Multi Layer Perceptron (MLP) learn that a Single Layer Perceptron cannot?
What can a Multi Layer Perceptron (MLP) learn that a Single Layer Perceptron cannot?
What is represented by a range of architectures in deep learning?
What is represented by a range of architectures in deep learning?
What is the primary characteristic of a Feed-forward Neural Network?
What is the primary characteristic of a Feed-forward Neural Network?
Who showed that every Boolean function can be implemented?
Who showed that every Boolean function can be implemented?
What is the purpose of an activation function in a neural network?
What is the purpose of an activation function in a neural network?
What is the function of the bias weight in a single layer perceptron?
What is the function of the bias weight in a single layer perceptron?
What is the output of the step function or threshold function when x is greater than or equal to 0?
What is the output of the step function or threshold function when x is greater than or equal to 0?
What is a simplified model of real neurons, used to develop understanding of what networks of simple units can do?
What is a simplified model of real neurons, used to develop understanding of what networks of simple units can do?
What determines whether a neuron is activated or not in a neural network?
What determines whether a neuron is activated or not in a neural network?
What is the name of the function that decides whether a neuron should be activated or not?
What is the name of the function that decides whether a neuron should be activated or not?
What is the process of prediction in a neural network using simpler mathematical operations?
What is the process of prediction in a neural network using simpler mathematical operations?
What is the type of neural network where the output of one layer is used as input to the next layer?
What is the type of neural network where the output of one layer is used as input to the next layer?
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Study Notes
Feed-forward Neural Network (FNN)
- A parameterized family of nonlinear functions
- Information moves in only one direction: from input nodes to output nodes, with no cycles or loops
- Types: Single Layer Perceptron (no hidden layer, can only learn linear functions) and Multi Layer Perceptron (one or more hidden layers, can learn non-linear functions)
Activation Functions
- Decide whether a neuron should be activated or not based on input value and threshold value
- Types:
- Step function or threshold function g(x)=1 if x>=0, 0 otherwise
- Sigmoid function g(x)=1/(1 + e^(-x)) and g'(x)=g(x) ∗(1− g(x))
- ReLU, TanH, etc.
Weight Adjustment
- Changing weights changes the function of the neural network
- Adjusting bias weight W0,i moves the threshold location
Deep Learning Architectures
- A spectrum of architectures for a range of problem areas
- Require large amounts of computing power and information to solve complicated issues
Implementing Logical Functions
- McCulloch and Pitts: every Boolean function can be implemented using neural networks
- Examples: AND, OR, NOT gates implementation using neural networks
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