Neural Networks Chapter: Convolutional and Recurrent Networks
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Questions and Answers

What type of data is not well-suited to be modeled by a generic multi-layer perceptron?

  • Sequential data (correct)
  • Numerical data
  • Categorical data
  • Spatial data

    • What type of neural network is designed to handle imaging data?

  • Recurrent neural networks
  • Generative Adversarial Networks
  • Autoencoders
  • Convolutional neural networks (correct)

    • Who is the author of the case study on bisphosphonate induced femur fractures?

  • Palmer LA
  • Keshavamurthy J.C. (correct)
  • Jones JP
  • Marčelja S.

    • What is the title of the paper written by Dalal and Triggs in 2005?

    <p>Histograms of Oriented Gradients for Human Detection</p> Signup and view all the answers

    In what year was the paper 'Backpropagation Applied to Handwritten Zip Code Recognition' published?

    <p>1989</p> Signup and view all the answers

    What is the name of the database of handwritten digit images for machine learning research?

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

    What is the name of the large-scale hierarchical image database?

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

    Who is the author of the paper 'The MNIST Database of Handwritten Digit Images for Machine Learning Research'?

    <p>Deng L.</p> Signup and view all the answers

    What is the primary function of the recurrent layer in a Vanilla RNN?

    <p>To maintain a hidden state</p> Signup and view all the answers

    What is the mathematical formula for the recurrence relation in a Vanilla RNN?

    <p>h_t = σ(W_x<em>x_t + W_h</em>h_{t-1} + b)</p> Signup and view all the answers

    What is the purpose of backpropagation through time (BPTT) in training a Vanilla RNN?

    <p>To update the model parameters</p> Signup and view all the answers

    What is a major limitation of Vanilla RNNs?

    <p>Difficulty in handling long-term dependencies</p> Signup and view all the answers

    What is the role of the hidden state in a Vanilla RNN?

    <p>To maintain the context of the sequence</p> Signup and view all the answers

    What is the architecture of a Vanilla RNN composed of?

    <p>Input layer, recurrent layer, and output layer</p> Signup and view all the answers

    Study Notes

    Introduction to Convolutional Neural Networks and Recurrent Neural Networks

    • Generic multi-layer perceptrons are not suitable for modeling data with spatial or sequential order, such as images and texts.
    • Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to handle imaging and text data respectively.

    Research on Convolutional Neural Networks

    • Keshavamurthy's case study on bisphosphonate induced femur fractures accessed in Aug 2022.
    • Marčelja's mathematical description of cortical cell responses in 1980 introduced the concept of simple receptive fields.
    • Jones and Palmer's 1987 evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex.
    • Dalal and Triggs' 2005 work on histograms of oriented gradients for human detection.
    • Lowe's 2004 research on distinctive image features from scale-invariant keypoints.
    • LeCun, Boser, Denker, et al.'s 1989 application of backpropagation to handwritten zip code recognition.

    Image Databases

    • The MNIST database, introduced by Deng in 2012, is a collection of handwritten digit images for machine learning research.
    • ImageNet, introduced by Deng, Dong, Socher, Li, Li, and Fei-Fei in 2009, is a large-scale hierarchical image database.

    Vanilla RNN

    Definition and Architecture

    • A Vanilla RNN is a simple type of Recurrent Neural Network (RNN) that processes sequences of input data
    • Also known as a Simple RNN or Basic RNN
    • Consists of an input layer, a recurrent layer (hidden state), and an output layer
    • Feedback connections from the recurrent layer to itself, allowing the network to maintain a hidden state

    Recurrence Relation

    • Defined as: h_t = σ(W_x*x_t + W_h*h_{t-1} + b)
    • h_t is the hidden state at time t
    • x_t is the input at time t
    • W_x and W_h are learnable weights
    • b is a bias term
    • σ is an activation function (e.g. tanh or sigmoid)

    Forward Pass

    • At each time step t, the network:
      • Computes the hidden state h_t using the recurrence relation
      • Computes the output y_t using the hidden state h_t
    • The hidden state h_t is used to compute the output y_t and also as input to the next time step

    Training

    • Trained using backpropagation through time (BPTT)
    • The network is unrolled over time, and the gradients are computed and accumulated at each time step
    • The gradients are then used to update the model parameters

    Limitations

    • Suffers from the vanishing gradient problem, making it difficult to train for long sequences
    • Not suitable for modeling long-term dependencies in sequences

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    Quiz Team

    Description

    This chapter discusses two popular neural network architectures designed to handle imaging and text data: convolutional neural networks and recurrent neural networks.

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