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PROJECT
Create Your Own Language Models from Scratch
In this project, we’ll build and compare simple n-gram models and a recurrent neural network (RNN) to generate realistic-sounding names from scratch.
You will learn to:
Develop basic language models using bigrams and trigrams.
Design and implement neural network architectures for language modeling.
Train a custom recurrent neural network (RNN) model for text generation.
Generate coherent text using the trained language models.
Skills
Data Science
Data Visualization
Text Preprocessing
Machine Learning
Neural Networks
Prerequisites
Good understanding of Python programming language
Basic knowledge of natural language processing (NLP)
Experience with data preprocessing and handling textual data in machine learning contexts
Familiarity with neural networks and deep learning frameworks, such as TensorFlow or PyTorch
Technologies
Python
PyTorch
Matplotlib
Project Description
In this project, we’ll learn how to build language models from scratch, using easy-to-follow techniques to generate text. We’ll focus on working with a dataset of names to show how different models can create realistic and interesting text. We’ll start with simple models (bigrams and trigrams) to see how they generate names, then move on to a more advanced RNN model to show how much more it can do.
In the first part, we’ll build a basic Bigram language model to create and optimize names using trigrams. This will help us understand the benefits of using more complex n-gram models. In the second part, we’ll use neural networks to improve our model. We’ll design a custom RNN to better understand the patterns in names. After training our RNN, we’ll use it to generate names, demonstrating the effectiveness of neural networks in creating more natural-sounding text.
Project Tasks
1
Foundations of Language Modeling
Task 0: Get Started
Task 1: Import Necessary Modules
Task 2: Load and Preprocess the Text Data
Task 3: Build and Visualize the Bigram Lookup Table
Task 4: Generate Names with the Bigram Language Model
Task 5: Generate Names Utilizing Trigrams
2
Enhance Language Models with NNs
Task 6: Define a Decoder and Convert Characters to Tensors
Task 7: Design the RNN Architecture for Language Modeling
Task 8: Write Functions to Generate the Text
Task 9: Train the Custom RNN Model
Congratulations!
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