This article discusses three techniques that practitioners could use to effectively start working with natural language processing (NLP). This will also give good visibility to people interested in having a sense of what NLP is about -- if you are an expert, please feel free to connect, comment, or suggest. At erreVol, we leverage similar tools to extract useful insights from transcripts of earnings reports of public corporations -- the interested reader can go test the platform. Note, we will present lines of codes for the reader interested in replicating or using what is presented below. Otherwise, please feel free to skip those technical lines as the reading should result seamless.

This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook's AI Research Lab that is powerful, easy to learn, and very versatile. When it comes to training material, however, PyTorch lags behind TensorFlow, Google's flagship deep learning library. There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions.

Sign in to report inappropriate content. In this 2 hour tutorial, we will briefly introduce TensorFlow 2.0, then dive in to training neural networks. This tutorial is targeted at folks new to TensorFlow, and/or Deep Learning, but includes things for experienced folks as well.