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Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset.
Features
- CNN architecture adapted for text
- TensorFlow implementation of a research model
- Embeddings and feature extraction layers
- Configurable training and hyperparameter options
- Data preprocessing utilities
- Baseline for text classification experimentation
Project Samples
