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Questions and Answers
Which function of LSTM determines the part of the cell state that influences the output?
Which function of LSTM determines the part of the cell state that influences the output?
What advantage does the Gated Recurrent Unit (GRU) have over LSTM in terms of efficiency?
What advantage does the Gated Recurrent Unit (GRU) have over LSTM in terms of efficiency?
In which scenario is it recommended to use LSTM instead of GRU?
In which scenario is it recommended to use LSTM instead of GRU?
What does the reset gate in a Gated Recurrent Unit (GRU) control?
What does the reset gate in a Gated Recurrent Unit (GRU) control?
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What unique feature does the Peephole LSTM offer that standard LSTM does not?
What unique feature does the Peephole LSTM offer that standard LSTM does not?
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What is the primary function of the memory cell in a Long Short-Term Memory (LSTM) network?
What is the primary function of the memory cell in a Long Short-Term Memory (LSTM) network?
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Which of the following best describes the purpose of gradient clipping in neural networks?
Which of the following best describes the purpose of gradient clipping in neural networks?
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What advantage do Gated Recurrent Units (GRUs) provide over traditional RNNs?
What advantage do Gated Recurrent Units (GRUs) provide over traditional RNNs?
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What does gradient scaling achieve in the context of neural networks?
What does gradient scaling achieve in the context of neural networks?
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Which gate in an LSTM controls the flow of information into the cell state?
Which gate in an LSTM controls the flow of information into the cell state?
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Study Notes
Image Captioning
- Produces descriptive sentences from images using input feature vectors derived from Convolutional Neural Networks (CNN).
- Examples of output from image captioning include concise phrases like “The dog is hiding.”
RNN Input Stages
- Init-Inject: Image vector serves as the RNN's initial hidden state vector, requiring size alignment with the RNN hidden state.
- Pre-Inject: Image vector treated as the first word in input sequence; requires size alignment with word vectors.
- Par-Inject: Inputs image vector simultaneously with word vectors, allowing varying vectors for words and not needing presence at every step.
- Merge: RNN does not access the image vector during processing; image is added to language model post-encoding.
Back-Propagation Techniques
- Back-Propagation Through Time (BPTT): Unfolds RNN into a feed-forward network to compute gradients across the entire sequence.
- Truncated BPTT: Processes forward and backward in chunks; retains hidden states across sequential batches.
RNN Architectures
- Multi-layer RNNs can solve complex sequential problems by stacking hidden layers.
- Bi-directional RNNs offer forward and reverse processing of sequences, enhancing features for applications like speech recognition.
Gradients in RNNs
- Vanishing Gradients: Gradients shrink to near-zero due to model complexity across many layers.
- Exploding Gradients: Gradients grow uncontrollably, leading to numerical instability during training.
Gradient Management
- Gradient Scaling: Normalizes gradient vector to a defined norm, often 1.0.
- Gradient Clipping: Restricts gradient values to remain within a specified range, improving training stability.
Gated RNNs
- Include Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, effective in managing long sequences by preventing vanishing gradients.
- LSTM utilizes a memory cell for maintaining information over time and has several gates (input, forget, output) to control data flow.
LSTM Specifics
- Employs a structure that allows the model to "forget" information and makes predictions based on retained state.
- Introduces concepts of Constant Error Flow, facilitating better gradient management.
GRU Overview
- Simplifies LSTM by combining the forget and input gates into a single update gate; merges cell and hidden states.
- Fewer parameters lead to reduced memory usage and potentially faster training, with competitive performance against LSTM.
Comparison Between LSTM and GRU
- GRUs are more efficient with memory and processing speed; LSTMs excel on larger datasets and longer sequences.
- Choice between GRU and LSTM depends on application constraints like memory and data sequence length.
Recurrent Neural Networks (RNNs)
- Designed for sequential data processing, RNNs can handle varying lengths and structures.
- Different from fully connected feed-forward networks, RNNs share weights over time steps, allowing them to learn sequential relationships.
Applications of RNNs
- Versatile use cases include language modeling, machine translation, stock market predictions, speech recognition, image caption generation, video tagging, text summarization, and medical data analysis.
Basic RNN Task
- Core functionality involves predicting future values based on past inputs, mapping previous states into fixed-length vectors to inform future predictions.
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Description
Explore the techniques of image captioning through convolutional neural networks and recurrent neural networks. This quiz covers the core concepts and methodologies used to generate descriptive sentences from visual content. Test your understanding of how sentiment classification intertwines with image processing.
