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Questions and Answers
What is the main purpose of the backpropagation algorithm in a neural network?
What is the main purpose of the backpropagation algorithm in a neural network?
- To modify the weights and biases of the neural network (correct)
- To calculate the output of the hidden layers
- To initialize the weights and biases of the neural network
- To determine the mean squared error loss
What is the order of calculations in a feedforward neural network?
What is the order of calculations in a feedforward neural network?
- o1, h1, h2
- h1, h2, o1 (correct)
- o1, h2, h1
- h2, o1, h1
What is the purpose of step 2 in the training algorithm?
What is the purpose of step 2 in the training algorithm?
- To determine the mean squared error loss (correct)
- To modify the weights and biases of the neural network
- To initialize the weights and biases of the neural network
- To calculate the output of the hidden layers
What are the weights and biases initialized in the training algorithm?
What are the weights and biases initialized in the training algorithm?
What is calculated in backpropagation after feedforward?
What is calculated in backpropagation after feedforward?
What is the purpose of the repeat loop in the training algorithm?
What is the purpose of the repeat loop in the training algorithm?
What is the first step in the process of a neuron?
What is the first step in the process of a neuron?
What is the purpose of an activation function?
What is the purpose of an activation function?
What is the range of the sigmoid function?
What is the range of the sigmoid function?
What happens to big negative numbers when passed through the sigmoid function?
What happens to big negative numbers when passed through the sigmoid function?
What is the output of the unit step function at x = 7?
What is the output of the unit step function at x = 7?
What type of problem can a simple neuron solve?
What type of problem can a simple neuron solve?
What is the formula for combining the weighted inputs and bias?
What is the formula for combining the weighted inputs and bias?
What is the value of the hyperbolic tangent function at x = 7?
What is the value of the hyperbolic tangent function at x = 7?
What is the primary limitation of a simple neuron (Perceptron)?
What is the primary limitation of a simple neuron (Perceptron)?
What is the solution to the XOR problem in neural networks?
What is the solution to the XOR problem in neural networks?
What is the primary function of a hidden layer in a neural network?
What is the primary function of a hidden layer in a neural network?
What is the term for a neural network with multiple hidden layers?
What is the term for a neural network with multiple hidden layers?
What is the purpose of the output layer in a neural network?
What is the purpose of the output layer in a neural network?
What is the first step in training a neural network?
What is the first step in training a neural network?
How is the sex of an individual represented in the training data?
How is the sex of an individual represented in the training data?
What is the purpose of the loss function in training a neural network?
What is the purpose of the loss function in training a neural network?
What is the output of the neural network in the given example?
What is the output of the neural network in the given example?
What is the purpose of shifting the data in the training process?
What is the purpose of shifting the data in the training process?
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Study Notes
Neural Networks
- A neural network is a bunch of neurons connected together.
- It can have multiple layers, including an input layer, one or more hidden layers, and an output layer.
- A hidden layer is any layer between the input layer and output layer.
Training a Neural Network
- The process involves three steps: initialize weights, repeat forward and backward passes, and modify weights.
- Forward pass: calculate outputs from inputs using current weights.
- Backward pass: calculate error and modify weights to minimize error.
Feedforward Neural Network
- Calculate outputs from inputs using current weights.
- Output of one layer is used as input to the next layer.
Back Propagation
- Calculate error and modify weights to minimize error.
- Calculate errors for each layer, starting from the output layer and moving backwards.
XOR Problem
- A simple neuron (perceptron) cannot classify XOR problem.
- Solution is to add a hidden layer to create a multilayer perceptron (MLP).
Multilayer Perceptron (MLP)
- A feedforward neural network with one or more hidden layers.
- Can solve non-linear problems like XOR.
Example Neural Network
- Given a neural network with 2 inputs, 2 hidden neurons, and 1 output neuron.
- Calculate output using given weights and activation functions.
Building Blocks: Neurons
- A neuron takes inputs, applies weights, adds bias, and passes through an activation function.
- Activation function is used to turn an unbounded input into a predictable output.
Activation Function
- A commonly used activation function is the sigmoid function.
- Sigmoid function outputs numbers in the range (0,1) and compresses (-∞, +∞) to (0,1).
Linear Classifier
- A simple neuron can solve linear classifier problems like OR.
- But it cannot solve non-linear problems like XOR.
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