Deep Neural Networks II - CNNs and RNNs
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What is a common issue faced when training RNNs?

  • Easy convergence of training
  • High computational efficiency
  • Overfitting to training data
  • Exploding or vanishing gradient problem (correct)

    • In RNNs, what role does the input sequence length play?

  • It determines the number of output neurons
  • It has no impact on training
  • It directly affects the learning rate
  • It functions like depth in the network (correct)

    • Why does the value of tanh' tend to be less than 1 in RNNs?

  • It reduces the chances of overfitting
  • It prevents the model from learning too quickly
  • It is designed to normalize input values
  • It leads to the vanishing gradient issue (correct)

    • What characteristic complicates the training of RNNs?

    <p>Recurrent connections with shared weights</p> Signup and view all the answers

    What mathematical representation is highlighted in the context of RNNs?

    <p>Backpropagation through time equation</p> Signup and view all the answers

    What is a key characteristic of self-supervised learning?

    <p>It relies on implicit labels extracted from data points.</p> Signup and view all the answers

    What is the primary benefit of transfer learning?

    <p>It facilitates the reuse of pretrained models on unrelated tasks.</p> Signup and view all the answers

    What is a common challenge in designing recurrent neural networks?

    <p>Defining network architecture for variable-length inputs.</p> Signup and view all the answers

    Which application is NOT commonly associated with NLP?

    <p>Image recognition</p> Signup and view all the answers

    Which technique is used to generate synthetic data?

    <p>Data augmentation</p> Signup and view all the answers

    Which aspect is crucial for successful supervised learning?

    <p>Availability of labeled training data.</p> Signup and view all the answers

    What is an essential property of a convolutional neural network (CNN)?

    <p>Many CNN architectures and pretrained models are available.</p> Signup and view all the answers

    What problem does the challenge of 'remembering past information' address?

    <p>Utilizing historical data for future predictions.</p> Signup and view all the answers

    What is a key characteristic of the pooling layer in CNN architectures?

    <p>It has hyperparameters like size and stride.</p> Signup and view all the answers

    Which layers in a CNN are primarily responsible for feature extraction?

    <p>Convolutional layers and pooling layers.</p> Signup and view all the answers

    What occurs during the transformation of a 3D volume in a CNN?

    <p>A differentiable function transforms the input volume.</p> Signup and view all the answers

    What is typically the last stage of a ConvNet architecture?

    <p>Fully connected layers.</p> Signup and view all the answers

    Which statement about back-propagation in CNNs is correct?

    <p>It involves optimizing the learning process using methods like SGD and Adam.</p> Signup and view all the answers

    Which of the following is NOT a type of layer commonly found in CNN architectures?

    <p>Recurrent layer.</p> Signup and view all the answers

    What defines the output of each layer in a CNN?

    <p>Each layer applies a differentiable function to transform the input volume.</p> Signup and view all the answers

    What does end-to-end learning in CNNs imply?

    <p>The entire network can be trained simultaneously from input to output.</p> Signup and view all the answers

    What problem do LSTMs primarily address?

    <p>Short-term memory limitations in standard RNNs</p> Signup and view all the answers

    Which of the following is NOT a practical measure to handle exploding gradients?

    <p>Using LSTMs instead of vanilla RNNs</p> Signup and view all the answers

    What role does the 'forget gate' play in an LSTM?

    <p>It determines which information is discarded from the cell state</p> Signup and view all the answers

    What is a key characteristic of the gating mechanism in LSTMs?

    <p>Gates are used to learn and decide on information flow</p> Signup and view all the answers

    Which of the following is true about cell states in LSTMs?

    <p>Cell states replace the hidden state of standard RNNs</p> Signup and view all the answers

    Which statement accurately describes Gated Recurrent Units (GRUs) compared to LSTMs?

    <p>GRUs are redundant compared to LSTMs</p> Signup and view all the answers

    Which mathematical expression represents the output of the LSTM cell?

    <p>$h = o \circ \sigma(c)$</p> Signup and view all the answers

    Which characteristic of the LSTM’s gating mechanism allows it to handle the vanishing gradient problem?

    <p>Gates create parallel pathways for information flow</p> Signup and view all the answers

    What is the purpose of one-hot encoding in the context of ground truth labels?

    <p>To represent categorical data as binary vectors</p> Signup and view all the answers

    What is indicated by the special token at the beginning of a sequence?

    <p>Start of sequence</p> Signup and view all the answers

    What is a significant drawback of traditional RNNs in processing input sequences?

    <p>Information from earlier inputs is often forgotten</p> Signup and view all the answers

    How does the attention mechanism enhance the RNN's decoding process?

    <p>By allowing the decoder to focus on relevant hidden states</p> Signup and view all the answers

    What does the context vector for the decoder do?

    <p>It varies based on the importance of hidden states</p> Signup and view all the answers

    Which method can be used to determine how much attention to give to different encoder states?

    <p>Neural networks or similarity measures</p> Signup and view all the answers

    What is one of the main ideas behind improving the RNN's decoder performance?

    <p>Incorporating attention to leverage all hidden states</p> Signup and view all the answers

    Why do RNNs tend to lose information from earlier inputs during encoding?

    <p>They compress sequences into a single embedding</p> Signup and view all the answers

    What is a primary limitation of using fully-connected (FC) layers for large images?

    <p>They generate a massive number of parameters.</p> Signup and view all the answers

    What does convolution primarily exploit in image processing?

    <p>Spatial locality of pixels.</p> Signup and view all the answers

    What is the function of a filter (kernel) in a convolutional layer?

    <p>To compute the dot product with input locations.</p> Signup and view all the answers

    How do convolutional layers differ from fully-connected layers?

    <p>Convolutional layers consider local pixel relationships.</p> Signup and view all the answers

    What effect does increasing the stride in a convolutional operation have?

    <p>It reduces the output feature map size.</p> Signup and view all the answers

    What is the main purpose of pooling layers in a CNN?

    <p>To aggregate values and reduce representation size.</p> Signup and view all the answers

    What is a characteristic feature of convolutional neural networks (CNNs)?

    <p>They replace matrix multiplication with convolution.</p> Signup and view all the answers

    What is the typical outcome when using multiple filters in a convolutional layer?

    <p>Multiple output feature maps are generated.</p> Signup and view all the answers

    What benefit does zero-padding provide in convolutional operations?

    <p>To avoid losing edge information.</p> Signup and view all the answers

    What type of data can convolutional networks process effectively?

    <p>Any input laid out on a grid (e.g., images, time-series).</p> Signup and view all the answers

    In a CNN, what is typically true regarding the learned filters as layers increase?

    <p>Filters learn a hierarchy from lower to higher spatial features.</p> Signup and view all the answers

    What is the role of hyperparameters such as stride and padding in convolutional layers?

    <p>They control the size of the output feature map and computational efficiency.</p> Signup and view all the answers

    What is a key feature of gated recurrent networks (RNNs)?

    <p>They have mechanisms for handling long-range dependencies in sequences.</p> Signup and view all the answers

    Study Notes

    Deep Neural Networks II - CNNs and RNNs

    • Kyung-Ah Sohn, Ajou University
    • Deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are covered.

    Table of Contents

    • Convolutional Neural Networks (CNNs)
      • CNN architecture, training and regularization
      • Named CNNs
      • Transfer Learning
    • Recurrent Neural Networks (RNNs)
      • Sequence-based prediction
      • Gated RNNs
      • Sequence-to-sequence problem

    Convolutional Neural Networks (CNNs)

    • CNNs are specialized neural networks for grid-like data such as images.
    • They scale up neural networks for processing very large images and/or video sequences.
    • Convolutions are used in CNNs.

    Recurrent Neural Networks (RNNs)

    • RNNs are useful for processing sequences of vectors.
    • They use a recurrence formula at each time step for processing a sequence.
    • The same function and set of parameters are used at every time step.
    • RNNs can return a sequence as output or the last output.
      • They have various applications in natural language processing (NLP).
    • RNNs suffer from the vanishing gradient problem, especially when sequences are long.

    Applications of RNNs

    • Sentiment classification
    • Speech recognition
    • Machine translation
    • Text generation

    Challenges of RNNs

    • Defining network architecture to handle variable input lengths.
    • Handling past information and using it for future prediction.

    Example: Sequence Classification

    • Input sequence is used to generate output.
    • The output can be a classification or regression prediction based on various variables.

    Sequence Classification: Input Encoding

    • The input sequence (vector representations of input sequences) is encoded into hidden state.
    • The input sequence is converted into a vector and used in calculation of the output.
    • Common weights are used across all time steps.

    Sequence Classification

    • The entire sequence is encoded as the last hidden state.
    • A classifier or regressor maps the encoding (the last hidden state or latent representation) to the output.

    Recurrent Neural Network

    • Recurrence formula is applied at each time step during the process of a sequence of vectors.
    • This formula involves a new state, old state, and input vector at the time step.

    RNN Output

    • The recurrent layer can return a sequence as output.
    • Another option for an output is the last output value.

    Different Categories of Sequence Modeling

    • One-to-one (e.g., image captioning)
    • One-to-many (e.g., sentiment analysis)
    • Many-to-one (e.g., machine translation)
    • Many-to-many (e.g., video classification)

    RNN is Hard to Train

    • Real-world experiments, like those on language modeling, show that RNNs can sometimes be hard to train.

    Exploding/Vanishing Gradient Problem in RNNs

    • During backpropagation, the gradient can either explode or vanish, depending on the weights.
    • The value of the tanh activation function is usually less than one.

    Practical Measures to address RNN Training Issues

    • Exploding gradients can be clipped to a threshold.
    • Training can use truncated backpropagation through time.
    • Learning rate can be adjusted.
    • Vanishing gradients are harder to detect and resolve.
    • Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs) are used to tackle this problem.

    Long Short-Term Memory (LSTM)

    • LSTMs overcome some of the short-term memory problems of standard RNNs
    • LSTMs have a memory cell that functions as the hidden state of standard RNNs.
    • A gating mechanism controls information flow.

    Gating Mechanism

    • A vector controls how much information will be kept or discarded.
    • The sigmoid function's output value helps in this selection (between 0 and 1).

    LSTM: Using Gates & Cell State

    • Gradient vanishing is avoided by a new set of hidden states—cell state (C)—with a "highway" detouring the FC layer.
    • Three types of gates (forget, input, output) control information flow.

    Gated Recurrent Unit (GRU)

    • GRUs are a more simplified architecture than LSTMs.
    • GRUs combine the forget and input gates into a single update gate.
    • GRUs merge the cell state and hidden state.
    • GRUs usually have fewer parameters than LSTMs.

    LSTM vs. GRU

    • LSTMs and GRUs are commonly used gated RNN variants.
    • LSTMs are a great default choice when speed and fewer parameters aren't primary considerations.

    Common Variations of RNNs

    • Bi-directional RNNs
    • Deep (multi-layer) RNNs
    • Handling vanishing gradients by introducing skip connections.

    Example: LSTM for Sequence Classification

    • Implementation in Keras provides information on how to use an embedding layer, LSTM layer, and a dense layer for sequences in a classification task.

    Sequence-to-Sequence Problems

    • Seq2seq problems, like machine translation, have different input/output sequence lengths.
    • There isn't always a one-to-one correspondence between input and output tokens.
    • An encoder-decoder structure can be used to address the differing input and output sequence lengths and non- one-to-one correspondence.

    Encoder-Decoder Structure

    • Encoder compresses the entire input sequence into a vector representation (embedding).
    • Decoder generates the output from this embedding.
    • This allows for variable length input/outputs.

    Decoder RNN: Autoregressive Generation

    • Autoregressive generation in decoders is done by using Softmax activation to generate the probabilities of each output token.
    • Probability of the next token depends on previously generated tokens.

    Information Loss in RNNs

    • The entire sequence is encoded in a single embedding, causing information related to earlier inputs to be lost.
    • Techniques to handle this information loss include attention mechanisms.

    Attention Mechanism

    • The attention mechanism allows the decoder to focus on important parts of the input sequence, and the encoder's hidden states.
    • The context vector varies for each step of the decoder.

    Attention Heatmap

    • The attention heatmap shows the relative importance (weight) given to each input word when generating a target word in machine translation.

    RNN Encoder-Decoder (with/without attention)

    • Shows how the encoder compresses the entire input sequence into a fixed-length vector.
    • Shows how the decoder uses the encoded vector's information during the generation of the output.
      • The use of attention helps the decoder focus on the relevant parts of the input sequence to produce the output sequence.

    Attention Function

    • Used for computations within a seq2seq RNN model.
    • Q (query), K (key), and V (value) are inputs.
    • Used to focus on the weights of the encoded-sequence parts when computing the encoded vector.
    • The values of Q, K, and V have the same dimensionality.

    Attention Methods

    • Options for scoring similarities include dot-product attention, learnable weighted dot-product attention, or concatenation.

    Attention-Based Seq2Seq Model

    • Models dependencies without regard to their distance in the input/output sequence.
    • Dealing with long sequences is a challenge and often hard to parallelize.

    Real-World Success of RNNs and Transformers

    • LSTMs and GRUs improved performance in machine translation tasks.
    • Transformers have shown greater strength and wider adoption in real-world applications.

    RNN: Summary

    • RNNs are good for processing sequence data.
    • Short-term memory problems can be mitigated with gating mechanisms.
    • Multi-layer RNNs can be powerful but may need skip/dense connections
    • Attention mechanisms can be vital for complex seq2seq problems.

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    Deep Neural Networks II - CNNs and RNNs - PDF

    Description

    Explore the intricacies of Deep Neural Networks focusing on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This quiz covers architectures, training methods, and applications in sequence prediction and image processing.

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