Building a Word-Level Language Model Using RNNs in PyTorch - Lab 4.4

Learn to implement a word-level language model using Recurrent Neural Networks (RNNs) in PyTorch through a hands-on tutorial that guides you through working with fairy tales datasets, tokenization techniques, and vocabulary creation. Master the process of preparing data for self-supervised learning, constructing and training the model architecture, and explore advanced concepts like temperature and top-p sampling for text generation. Dive into word embeddings while following along with the provided Colab notebook, which includes practical examples and comprehensive code implementations. Progress through key concepts including model creation, text generation techniques, and the training process, all demonstrated using a fairy tales dataset to build a functional language model from scratch.