This article was written by Jon Krohn.
At untapt, all of our models involve Natural Language Processing (NLP) in one way or another. Our algorithms consider the natural, written language of our users’ work experience and, based on real-world decisions that hiring managers have made, we can assign a probability that any given job applicant will be invited to interview for a given job opportunity.
With the breadth and nuance of natural language that job-seekers provide, these are computationally complex problems. We have found deep learning approaches to be uniquely well-suited to solving them. Deep learning algorithms:
To share my love of deep learning for NLP, I have created five hours of video tutorial content paired with hands-on Jupyter notebooks. Following on from my acclaimed Deep Learning with TensorFlow LiveLessons, which introduced the fundamentals of artificial neural networks, my Deep Learning for Natural Language Processing LiveLessons similarly embrace interactivity and intuition, enabling you to rapidly develop a specialization in state-of-the-art NLP.
These tutorials are for you if you’d like to learn how to:
Below is a summary of the topics covered over the course of my five Deep Learning for NLP lessons (full breakdown detailed in my GitHub repository):
Lesson One: Introduction to Deep Learning for Natural Language Processing
Lesson Two: Word Vectors
Lesson Thee: Modeling Natural Language Data
To read the full original article click here. For more deep learning related articles on DSC click here.
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