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What type of data is not well-suited to be modeled by a generic multi-layer perceptron?
What type of neural network is designed to handle imaging data?
Who is the author of the case study on bisphosphonate induced femur fractures?
What is the title of the paper written by Dalal and Triggs in 2005?
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In what year was the paper 'Backpropagation Applied to Handwritten Zip Code Recognition' published?
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What is the name of the database of handwritten digit images for machine learning research?
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What is the name of the large-scale hierarchical image database?
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Who is the author of the paper 'The MNIST Database of Handwritten Digit Images for Machine Learning Research'?
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What is the primary function of the recurrent layer in a Vanilla RNN?
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What is the mathematical formula for the recurrence relation in a Vanilla RNN?
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What is the purpose of backpropagation through time (BPTT) in training a Vanilla RNN?
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What is a major limitation of Vanilla RNNs?
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What is the role of the hidden state in a Vanilla RNN?
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What is the architecture of a Vanilla RNN composed of?
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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_tis the hidden state at timet -
x_tis the input at timet -
W_xandW_hare learnable weights -
bis a bias term -
σis an activation function (e.g. tanh or sigmoid)
Forward Pass
- At each time step
t, the network:- Computes the hidden state
h_tusing the recurrence relation - Computes the output
y_tusing the hidden stateh_t
- Computes the hidden state
- The hidden state
h_tis used to compute the outputy_tand 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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Description
This chapter discusses two popular neural network architectures designed to handle imaging and text data: convolutional neural networks and recurrent neural networks.
