5. Issues and Techniques in Deep Learning 2 - 28012024 - RMSProp vs Adam Update in Neural Networks
32 Questions
1 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 a key benefit of using the ReLU activation function in deep neural networks?

  • It is not biologically plausible
  • It saturates in the positive region
  • It is computationally slower
  • It helps prevent vanishing gradient problems (correct)

    • Which activation function has been the most popular for deep neural networks since 2012?

  • ReLU (correct)
  • Leaky ReLU
  • Tanh
  • Sigmoid

    • What is a common issue with gradient descent when the learning rate is very slow?

  • It converges quickly
  • It is computationally faster
  • It settles at local minima too easily (correct)
  • It bounces off

    • What makes a fast learning rate problematic in gradient descent?

    <p>It bounces off the optimization path</p> Signup and view all the answers

    Why is weight initialization important in deep neural networks?

    <p>It affects how quickly the network learns</p> Signup and view all the answers

    What problem does ReLU help address in deep neural networks?

    <p>Vanishing gradient problem</p> Signup and view all the answers

    What two hyperparameters are used in the Adam (adaptive moment) update?

    <p>Momentum and squared momentum</p> Signup and view all the answers

    In the Adam update equation, what does 𝑤𝑡+1 represent?

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

    What is the primary purpose of implementing beta corrections to 𝑣𝑡+1 and 𝑟𝑡+1 in the Adam update equation?

    <p>To stabilize the algorithm convergence</p> Signup and view all the answers

    What role does the hyperparameter squared momentum (beta 2) play in the Adam update?

    <p>It controls the decay rate of historical gradients</p> Signup and view all the answers

    Which component in the Adam update equation is responsible for incorporating past gradients into the optimization process?

    <p>Momentum (beta 1)</p> Signup and view all the answers

    What distinguishes the Adam update from RMSProp in terms of optimization performance?

    <p>Adam incorporates past gradients unlike RMSProp</p> Signup and view all the answers

    What is a key issue with weight initialization where all weights are set to zero?

    <p>All updates will be the same because all outputs will be the same</p> Signup and view all the answers

    Which regularization technique involves introducing noise into the training data to prevent overfitting?

    <p>Dropout</p> Signup and view all the answers

    What is the purpose of Xavier/Glorot weight initialization in deep neural networks?

    <p>To avoid exploding/vanishing gradients</p> Signup and view all the answers

    What does L1 regularization encourage in neural networks?

    <p>Feature selection by driving some weights to exactly zero</p> Signup and view all the answers

    How does batch normalization help with training deep neural networks?

    <p>By normalizing the input to each layer of the network</p> Signup and view all the answers

    What is the main risk associated with using too many 𝛽𝑖 terms in a regression model?

    <p>Overfitting the data by capturing noise rather than signal</p> Signup and view all the answers

    What does momentum in gradient descent indicate?

    <p>How much importance is given to past values</p> Signup and view all the answers

    What is the purpose of implementing RMS Prop in SGD with Momentum?

    <p>To decrease the update if the average update of a weight is high</p> Signup and view all the answers

    How does RMS Prop differ from taking just the gradient for updating weights?

    <p>It considers EMA of gradient square instead of just gradient</p> Signup and view all the answers

    In gradient descent with momentum, what does the term 'rho' represent?

    <p>How many past values are taken into account for averaging</p> Signup and view all the answers

    Why is it important to avoid zig-zag movements in gradient descent?

    <p>To improve convergence speed</p> Signup and view all the answers

    What is the role of momentum in updating weights in gradient descent?

    <p>To take the average of past few gradients</p> Signup and view all the answers

    What is the purpose of Regularization in Standard Least Squares Regression?

    <p>To minimize the sum of squared errors</p> Signup and view all the answers

    In the context of Regularization, what does penalizing large coefficients help prevent?

    <p>Overfitting</p> Signup and view all the answers

    What is the key difference between Ridge and Lasso regularization functions?

    <p>Ridge has infinite lambda for no model, Lasso has high lambda for simplest models</p> Signup and view all the answers

    What happens to the model complexity as lambda value increases in Regularization?

    <p>Model complexity decreases</p> Signup and view all the answers

    Which term refers to the vector of all training responses in the context of Regularization?

    <p>$y$</p> Signup and view all the answers

    What does N represent in the equation for Regularization?

    <h1>of training samples</h1> Signup and view all the answers

    What is the formula used to minimize the sum of squared errors with regularization included?

    <p>$ ext{min} igg( ext{sum}(y - Xeta) + ext{lambda} imes eta_jigg)$</p> Signup and view all the answers

    How does high lambda value affect the complexity of models in Regularization?

    <p>Decreases model complexity</p> Signup and view all the answers

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser