This is the repository for the LinkedIn Learning course Recurrent Neural Networks. The full course is available from LinkedIn Learning.

Get started with recurrent neural network (RNN) concepts in a simplified way and build simple applications with RNNs and Keras. RNN is a fast-growing domain within the AI world. Popular groundbreaking applications like language translation, speech synthesis, question answering, and text generation use RNNs as their base technology. Studying this technology, however, has several challenges. Most learning resources are math heavy and are difficult to navigate without good math skills. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, Kumaran Ponnambalam provides a simplified path to studying the basics of recurrent neural networks, allowing you to become productive quickly. Kumaran starts with a simplified introduction of RNN before walking through the process of building a model. He then covers the popular building blocks of RNN with GRUs, LSTMs, word embeddings, and transformers.

This repository contains the exercise files in a folder called "Exercise Files". This folder contains both the Data (.csv and .txt) files, as well as the Jupyter Notebook (.ipynb) files used in the course.

One file, glove.6B.50d.txt.zip, will need to be unzipped prior to use.

Follow the prompts in the video to load the correct exercise file.

1. To use these exercise files, you must have the following installed:
   • Python 3.8
   • Anaconda
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
3. Follow along with video 00_03 "Using the exercise files" for setup instructions.

Kumaran Ponnambalam

Check out my other courses on LinkedIn Learning.