Task: IMDB Movie Review Binary Classification Task

Technique Used in Sequencial and in Transformer and BiLSTM: Similarities and Differences

Table of Contents

1. Similarities
   • Data Processing Techniques
   • Deep Learning Techniques
   • Model Evaluation and Hyperparameter Tuning
   • Engineering Practices
2. Differences
   • Data Processing Techniques
   • Feature Engineering
   • Deep Learning Techniques
   • Regularization Techniques
   • Optimization Methods
   • Model Evaluation and Tuning
   • Resources and Tools
3. Summary Table
4. Conclusion

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1. Similarities

Data Processing Techniques

• Dataset Splitting:
  • Both use the IMDB dataset, splitting it into training, validation, and test sets.
• Text Representation:
  • Both use multi-hot encoding for representing text as binary feature vectors.
  • Transformer and BiLSTM extends this by incorporating more advanced vectorization techniques like Bag-of-Words (BoW), TF-IDF, and word embeddings.

Deep Learning Techniques

• Model Architectures:
  • Both use dense layers for text classification tasks.
  • Transformer and BiLSTM expands on this by introducing advanced architectures such as Transformer and Bidirectional LSTM (BiLSTM).
• Activation Functions:
  • Both tasks use ReLU for hidden layers and sigmoid for binary classification.
• Regularization:
  • Both apply dropout and early stopping to prevent overfitting.
• Optimization Methods:
  • Both utilize RMSprop and Adam optimizers with learning rate adjustments.

Model Evaluation and Hyperparameter Tuning

• Grid Search:
  • Both employ grid search for hyperparameter tuning (e.g., learning rates, regularization strength, batch size).
• Overfitting Detection:
  • Both monitor training and validation loss curves to identify overfitting.
• Visualization:
  • Both use Matplotlib to visualize training and validation performance metrics.

Engineering Practices

• Resource Optimization:
  • Both use callback functions (e.g., early stopping) and memory management techniques to optimize GPU resources.

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2. Differences

Data Processing Techniques

• Vocabulary Handling:
  • Sequential filters vocabulary by word frequency, retaining the top 10,000 words.
  • Transformer and BiLSTM uses the TextVectorization layer, supporting more advanced formats like N-grams and embedding vectors.
• Word Embeddings:
  • Sequential does not use pre-trained embeddings.
  • Transformer and BiLSTM incorporates GloVe pre-trained embeddings for semantic understanding and transfer learning.

Feature Engineering

• sequential:
  • Primarily uses multi-hot encoding to create binary feature vectors.
• Transformer and BiLSTM:
  • Expands feature engineering to include:
    • Bag-of-Words (BoW) for local context.
    • TF-IDF to weight term importance.
    • N-grams for capturing sequential information.

Deep Learning Techniques

• sequential:
  • Uses a simple feed-forward neural network with one hidden layer.
• Transformer and BiLSTM:
  • Implements more sophisticated architectures:
    • TransformerEncoder for long text sequences and global dependencies.
    • Bidirectional LSTM for capturing long-term dependencies.
    • Positional embeddings for retaining sequence information.

Regularization Techniques

• Sequential:
  • Applies L1, L2, and Elastic Net regularizations for weight penalties.
• Transformer and BiLSTM:
  • Uses weight initialization with pre-trained embeddings like GloVe.

Optimization Methods

• Learning Rate Strategies:
  • Sequential experiments with static learning rates.
  • Transformer and BiLSTM incorporates advanced learning rate scheduling and warm-up strategies.
• Model Saving:
  • Transformer and BiLSTM uses ModelCheckpoint for saving the best-performing models, which is absent in sequential.

Model Evaluation and Tuning

• Hyperparameters:
  • Sequential focuses on simpler hyperparameters such as learning rate, momentum, and regularization strength.
  • Transformer and BiLSTM introduces additional parameters like embedding dimensions, attention heads, and LSTM units.
• Comparison Experiments:
  • Sequential primarily tests variations of the feed-forward neural network.
  • Transformer and BiLSTM compares multiple architectures (Transformer, BiLSTM, BoW, TF-IDF).

Resources and Tools

• Visualization Tools:
  • Sequential uses Matplotlib for basic performance visualization.
  • Transformer and BiLSTM uses both Matplotlib and Seaborn for advanced data distribution and performance analysis.
• Model Training Environment:
  • Transformer and BiLSTM explicitly leverages T4 GPUs and addresses resource constraints for larger architectures like Transformers.

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3. Summary Table

Category	Similarities	Differences
Data Processing	Multi-hot encoding for binary vectors.	Transformer and BiLSTM uses TextVectorization with BoW, TF-IDF, and GloVe embeddings.
Feature Engineering	Both focus on text vectorization.	Sequential uses simple multi-hot encoding, while Transformer and BiLSTM integrates N-grams, BoW, and TF-IDF.
Deep Learning Techniques	Dense layers, ReLU activation, sigmoid for binary classification.	Transformer and BiLSTM adds Transformer, BiLSTM, and positional embeddings.
Regularization	Dropout and early stopping for overfitting prevention.	Sequential adds L1, L2, and Elastic Net penalties, while Transformer and BiLSTM uses GloVe for weight initialization.
Optimization	RMSprop and Adam optimizers.	Transformer and BiLSTM incorporates learning rate schedules and ModelCheckpoint for saving models.
Evaluation and Tuning	Grid search and loss visualization.	Transformer and BiLSTM introduces additional hyperparameters and compares multiple advanced architectures.
Resources and Tools	Matplotlib for visualizations.	Transformer and BiLSTM uses Seaborn for enhanced analysis and T4 GPUs for training large-scale models.

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