Title Generation using Recurrent Neural Networks (RNNs)

Choosing the perfect title for written work can be challenging. This project explores the use of recurrent neural networks (RNNs) to automatically generate compelling titles by learning from a large corpus of existing titles.

The project is flexible: you can generate titles or other text by changing the data source URL or specifying the local file path where your dataset is stored.

Example Titles Generated

Some example titles produced by the RNN model include:

• Function Classification Using Machine Learning Techniques
• Bayesian Approximation of Effective Machine Learning
• Data Classification With Machine Learning
• Computer Multi-agent Boltzmann Machine Learning
• Machine Learning Approaches for Visual Classification
• New Machine Learning for Astrophysics
• Neural Machine Learning for Medical Imaging
• Deep Similarity Learning Filters

---

🎯 Project Objectives

• Collect and preprocess a dataset of academic titles in the field of machine learning.
• Build and train a recurrent neural network (RNN) to learn title patterns.
• Generate new, plausible titles using the trained RNN model.
• Provide a flexible framework for text generation on custom datasets.

---

📦 Libraries Used / Tech Stack

• Python
• TensorFlow / Keras (for building and training the RNN)
• NumPy (numerical computing)
• Pandas (data handling and preprocessing)
• Matplotlib / Seaborn (visualization)

---