GOR-OM/WoC6.0-DL-name-generation-model-Om

Welcome to the WoC 6.0 Deep Learning Image Classification project! This repository aims to provide a comprehensive introduction to deep learning basics, focusing on image classification. By participating, you will gain hands-on experience in building and training neural networks for image recognition tasks.

WoC 6.0 - DL Name Generation Model (GOR-OM)

Winter of Code 6.0 Challenge: This repository hosts a name generation model utilizing advanced deep learning techniques for creating unique and customizable names.

Overview

In this project, we aim to build a name generation model using LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) neural networks. The project involves training these networks on a dataset of names to learn patterns and generate new, unique names. This hands-on experience provides insights into recurrent neural network architectures, data preprocessing for natural language processing, and the intricacies of model training and evaluation.

Why This Project/Technology?

• Deep Learning in NLP: Gain proficiency in handling sequential data, crucial in many NLP tasks.
• Hands-on Experience with RNNs: Understand the practical applications of LSTM and GRU, two powerful types of recurrent neural networks.
• Foundation for Complex Projects: Build a foundation in neural networks that can be extended to more complex tasks like language translation or text generation.

Concepts Covered

• Basics of Recurrent Neural Networks (RNNs)
• LSTM and GRU architectures
• Training and evaluating neural network models
• Tuning Hyperparameters

Overall Development Pipeline

1. Preprocessing: Implement functions to convert names into tensor format suitable for neural network training.
2. Model Building: Define LSTM and GRU models in PyTorch.
3. Training the Models: Train the models on the prepared dataset, adjusting parameters for optimal performance.
4. Name Generation: Use the trained models to generate new names.
5. Evaluation: Assess the performance and uniqueness of the generated names.

Tools and Technologies

• Python Programming Language
• PyTorch for building neural networks
• Jupyter Notebook or a similar IDE for development (Google Colab is one such option)

Learning Resources

• Python Official Documentation
• PyTorch Official Tutorials
• Natural Language Processing in Python by DataCamp

Phase-wise Division of the Project

Phase 1

1. Set up the development environment (Python, PyTorch, IDE) or use Google Colab.
2. Learn basic concepts of Python and PyTorch.
3. Preprocess the dataset.
4. Read about a simple RNN and then LSTM and GRU models.

Phase 2

1. Implement and train the LSTM and GRU models.
2. Develop the name generation functionality.
3. Evaluate and fine-tune the models by varying hyperparameters (learning rate, batch size, dropout, optimizer, etc.).
4. Document the results.

Additional Features (Optional)

• Change the architecture of LSTM slightly and observe the results.
• Visualize the training process using TensorBoard.
• Implement different types of sorting and filter options for the explore page.