Artificial Neural Networks (ANNs) Basics
26 Questions
0 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

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

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Description

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

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser