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Artificial Neural Networks (ANNs) Basics
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Artificial Neural Networks (ANNs) Basics

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Questions and Answers

What is the purpose of the transfer function in a neural network?

  • To compute the output function of a given input
  • To introduce non-linearity in the output (correct)
  • To evaluate the output by comparing it with the expected output
  • To adjust the weights during the learning process

    • What is the outcome of the evaluation phase in the learning process of an ANN?

  • The computation of the output function
  • The initialization of the weights randomly
  • The comparison of the actual output with the expected output (correct)
  • The adjustment of the weights

    • How are the initial values of the weights set in an ANN?

  • By hand, based on prior knowledge
  • Through a trial-and-error process
  • Through a complex mathematical formula
  • Randomly (correct)

    • What is the purpose of the adder function in a neuron?

    <p>To compute the weighted sum of the inputs</p> Signup and view all the answers

    What happens to the signal as it passes through the processing element in an ANN?

    <p>It is modified based on the weights and transfer function</p> Signup and view all the answers

    What is the role of the bias term in a neuron?

    <p>To shift the activation function of the neuron</p> Signup and view all the answers

    What is the purpose of the weights in an ANN?

    <p>To affect the strength of the signal as it passes through the processing element</p> Signup and view all the answers

    What is the output of the linear combiner in a neuron?

    <p>u = Σwjxj</p> Signup and view all the answers

    What is the outcome of the adjustment phase in the learning process of an ANN?

    <p>The adjustment of the weights</p> Signup and view all the answers

    What is the purpose of the activation function in a neuron?

    <p>To introduce non-linearity in the neuron output</p> Signup and view all the answers

    What is the role of bias in an ANN?

    <p>To shift the activation function</p> Signup and view all the answers

    What is the formula for the output of a neuron?

    <p>y = φ(v)</p> Signup and view all the answers

    How are the weights of a neuron updated?

    <p>Using the gradient descent algorithm</p> Signup and view all the answers

    What is the process of adjusting the weights in an ANN called?

    <p>Weight update</p> Signup and view all the answers

    What is the purpose of forward propagation in a neural network?

    <p>To compute the output of the network</p> Signup and view all the answers

    What is the role of the bias term in the output of a neuron?

    <p>To add a constant value to the neuron output</p> Signup and view all the answers

    What is the primary reason why ReLU is used more often than Sigmoid in deep neural networks?

    <p>ReLU trains much faster and accelerates the convergence of SGD</p> Signup and view all the answers

    What is the main difference between traditional machine learning and deep learning?

    <p>Traditional machine learning uses handcrafted features, deep learning learns them</p> Signup and view all the answers

    What is the primary purpose of the loss function in a neural network?

    <p>To compare predictions to true targets</p> Signup and view all the answers

    Why do sigmoid neurons saturate and kill gradients?

    <p>Because they have an S-shaped curve that saturates at 0 and 1</p> Signup and view all the answers

    What is overfitting in machine learning?

    <p>When a model is too complex and captures the noise in the data</p> Signup and view all the answers

    What is the main advantage of using ReLU over Sigmoid in deep neural networks?

    <p>ReLU prevents the vanishing gradient problem</p> Signup and view all the answers

    What is the purpose of an optimizer in a neural network?

    <p>To update the weights and bias terms</p> Signup and view all the answers

    What is the main difference between supervised and unsupervised learning?

    <p>Supervised learning has targets, unsupervised learning doesn't</p> Signup and view all the answers

    What is the purpose of the input data and targets in a neural network?

    <p>To map the input data to predictions</p> Signup and view all the answers

    What is the primary reason why deep neural networks can approximate complex functions?

    <p>Because they have more layers and neurons</p> Signup and view all the answers

    Study Notes

    Artificial Neural Networks (ANNs)

    • An ANN is an imitation of a human neuron
    • ANNs work by processing input signals through a series of nodes or "neurons"
    • Each neuron receives input signals, applies weights, and then applies an activation function to produce an output signal

    Artificial Neuron Model

    • Input signals are multiplied by weights (w1, w2, ..., wm)
    • The weighted sum is calculated (∑wixi)
    • An activation function (f(vk)) is applied to the weighted sum to produce an output signal (y)

    Characteristics of ANNs

    • ANNs can compute any computable function with the right network topology and weights
    • ANNs can learn from experience through trial-and-error
    • Learning is a continuous process of processing an input, evaluating the output, and adjusting the weights

    Bias as an Extra Input

    • Bias is an additional input (x0) with a weight (w0) that is always 1
    • The bias is used to shift the activation function and improve the fit of the model

    Neuron with Activation Function

    • A neuron consists of a set of links with weights, an adder function to compute the weighted sum, and an activation function to limit the output
    • Common activation functions include sigmoid and ReLU (Rectified Linear Unit)

    Activation Functions

    • Sigmoid: maps input to a value between 0 and 1, but can saturate and kill gradients during training
    • ReLU: maps input to a value above 0, is faster to train and more expressive than sigmoid

    Overfitting

    • When a model is too complex and performs well on the training data but poorly on new, unseen data
    • Can be prevented by regularization techniques, early stopping, and cross-validation

    Machine Learning Types

    • Supervised learning: uses labeled data to learn a mapping from input to output
    • Unsupervised learning: uses unlabeled data to discover patterns and relationships
    • Self-supervised learning: uses unlabeled data to learn representations that can be fine-tuned for specific tasks
    • Reinforcement learning: uses rewards and penalties to learn optimal actions in an environment

    Deep Neural Networks

    • Consist of multiple layers of artificial neurons
    • Can learn complex representations of data by composing multiple non-linear transformations
    • Can be used for image recognition, natural language processing, and other applications

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    Description

    Learn how Artificial Neural Networks (ANNs) work, including the model of an artificial neuron and the factors that affect signal strength.

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