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Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Programming](https://towardsdatascience.com/category/programming/) # Use Deep Learning to Generate Fantasy Names: Build a Language Model from Scratch Can a language model invent unique fantasy character names? Let's build it from scratch [Riccardo Andreoni](https://towardsdatascience.com/author/riccardo.andreoni/) September 22, 202311 min read ![Source: pixabay.com](../images/000.jpeg) Source: [pixabay.com](https://pixabay.com/illustrations/book-old-surreal-fantasy-pages-863418/) To truly grasp the intricacies of [**Language Models**](https://en.wikipedia.org/wiki/Language_model#:~:text=A%20language%20model%20is%20a,feedforward%20neural%20networks%20and%20transformers.) (LM) and become familiar with their underlying principles, there is no other way than rolling up our sleeves and starting to write code. In this article, I present the creation of a [**Recurrent Neural Network**](https://en.wikipedia.org/wiki/Recurrent_neural_network) (RNN) built entirely from scratch, without the aid of any deep learning library. Tensorflow, Keras, Pytorch make building deep and complex neural networks effortless. Undoubtedly, this is a great advantage for Machine Learning practitioners, however, this approach has the massive downside of leaving the functioning of those networks unclear as they happen "under the hood". This is why today we will perform the inspiring exercise of building a Language Model using only the [Numpy Python](https://numpy.org/) library! Learn this step by step with the interactive [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps. ## Understanding Recurrent Neural Networks and Language Models Standard fully connected neural networks are not suited for [Natural Language Processing](https://en.wikipedia.org/wiki/Natural_language_processing) (NLP) tasks like text generation. The main reasons are: * For NLP tasks, input and outputs may take different forms and dimensions. * Standard neural networks don't simultaneously use features learned at different steps of the network. The main breakthrough in AI application within the NLP field is undeniably represented by Recurrent Neural Networks (RNN). RNNs are a class of artificial neural networks particularly well suited for NLP tasks and text generation. The reason for their efficacy lies in their ability of capturing sequential dependencies in data. Human language deeply relies on considering the context and linking the first words in a sentence to the last ones. Consider these sentences: 1. _He said, "Teddy Roosevelt was the US President."_ 2. _He said, "Teddy bears are on sale!"_ The word "_Teddy_ " has a completely different meaning in the two sentences. We humans easily understand that by considering the context and the words written in the opposite part of the sentence. Surprisingly enough, **RNNs can do the same**! ### RNN Architecture The architecture of RNNs is fairly straightforward. They are made of sequential cells, each one of them takes as input a word _x_ (or a single character), outputs a word/character _y_ , and passes an activation _a_ to the next cell. ![RNN flowchart. Image by the author.](../images/001.png) RNN flowchart. Image by the author. What happens inside a RNN cell is more interesting. The steps are the following: 1. The activation of the previous cell is multiplied by some weights _W _aa_ 2. The input _x_ is multiplied by some weights _W _ax_ 3. The results of the previous steps are summed together and with a bias _b _a_ 4. An activation function like the hyperbolic tangent is applied to compute the activation which is passed to the next cell 5. The activation is multiplied by some weights _W _ya_ and summed with a bias _b _y_ 6. Finally, a softmax function is applied to the resulting vector and the output _y _hat_ is returned ![RNN cell flowchart. Image by the author.](../images/002.png) RNN cell flowchart. Image by the author. To summarize, the formulas for the activation and output are as follows: ![](../images/body-3e5ec5f71fd305b75977.png) ![](../images/body-649b98898e981d7292b7.png) I'm unable to provide a comprehensive theoretical introduction of RNNs and LMs here. For that, I refer you to the resources listed at the end of this article. Let's dive now into the actual Python code. The most important parts of the code will be explained in detail in this article. In order to keep the article concise, some intuitive parts are omitted. The entire commented code can be accessed in my [GitHub repository](https://github.com/andreoniriccardo/articles/tree/main/names-generator-RNN): > [**articles/names-generator-RNN at main · andreoniriccardo/articles**](https://github.com/andreoniriccardo/articles/tree/main/names-generator-RNN) ## Data Preparation Our goal is to teach a Language Model to invent novel fantasy character names. For this reason, the first thing we need to provide our Language Model is a database of actual fantasy names. The LM will be trained and will take inspiration from that. Thanks to this [Wikipedia page](https://en.wikipedia.org/wiki/List_of_Middle-earth_characters), we can easily access a list of all the characters mentioned in the _[Lord Of The Ring](https://en.wikipedia.org/wiki/The_Lord_of_the_Rings) _or_[The Hobbit](https://en.wikipedia.org/wiki/The_Hobbit)_ books. Using the [BeautifulSoup](https://pypi.org/project/beautifulsoup4/)and [Regex](https://docs.python.org/3/library/re.html) libraries, the following lines of code will collect the data: python ``` from bs4 import BeautifulSoup import requests import re url = "https://en.wikipedia.org/wiki/List_of_Middle-earth_characters" # Get the contents of the webpage in text format data = requests.get(url).text soup = BeautifulSoup(data,"html.parser") # Parse the HTML code to isolate rows containing character names names = soup.find_all('li') # Apply a second filter to isolate actual names names_2 = [] for i in names: if """