Learn Deep Learning by Building 15 Neural Network Projects in 2022 - KDnuggets

Neural networks have progressed enough to comprehend large amounts of text and generate entire sequences in continuation to a short input prompt. This notion has found immense use in generating product descriptions, promotional emails and also to generate training data to create models that can detect whether a given text was generated by AI. While models like GPT-3 have achieved the benchmark in this task, we can start from scratch using LSTMs. You can use text from the free ebooks available on Project Gutenberg. After cleaning and vectorizing the data, we can train a Sequential LSTM model on several batches of sentences. Once that is done, you can give the model a test sequence from the ebook and map the output vectors with the character set. You will see that the model was able to continue your input prompt and generate (hopefully) meaningful and relevant sentences.