We have been interacting through this book for almost 200 pages, but I realized that I have not introduced myself properly to you! I guess it's time. You already know some bits about me through the author profile in this book; however, I want to tell you a bit about the city I live in. I am based in South India, in a city called Bengaluru, also know as Bangalore. The city is known for its IT talent and population diversity. I love the city, as it is filled with loads of positive energy. Each day, I get to meet people from all walks of life—people from multiple ethnicities, multiple backgrounds, people who speak multiple languages, and so on. Kannada is the official language spoken in the state of Karnataka where Bangalore is located. Though I can speak bits and pieces of Kannada, my proficiency with speaking the language is not as good as a native Kannada speaker. Of course, this is an area of improvement for me and I am working on it. Like me, many other migrants that moved to the city from other places also face problems while conversing in Kannada. Interestingly, not knowing the language does not stop any of us from interacting with locals in their own language. Guess what comes to our rescue: mobile apps such as Google translate, Google text-to-speech, and the like. These applications are built on NLP technologies called machine translation and speech recognition. These technologies in turn work on things known as language models. Language models is the topic we will delve into in this chapter.

The objectives of the chapter include exploring the following topics:

• The need for language modeling to address natural language processing tasks
• The working principle of language models
• Application of language models
• Relationship between language modeling and neural networks
• Recurrent neural networks

• Differences between a normal feedforward network and a recurrent neural network
• Long short-term memory networks
• A project to autogenerate text using recurrent neural networks