This workshop teaches deep learning techniques for understanding textual input using natural language processing (NLP) through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows to perform neural network training on a fully-configured, GPU-accelerated workstation in the cloud.
The course teaches techniques to: train a neural network for text classification, build a linguistic style model to extract features from a given text document, and create a neural machine translation model for converting text from one language to another.
At the conclusion of the workshop, you will have an understanding of:
Why Deep Learning Institute Hands-On Training?
In order to receive NVIDIA DLI Certification on successful completion of the workshop, participants are presented with an exercise to assess subject matter competency.
Overview of Natural Language Processing
Overview of NLP challenges and how to tackle them with deep learning
We will cover distributed data representations, such as word embeddings using the word2vec algorithm.
Once trained, the word embeddings can be used for variety of problems, including text classification.
Text classification will be used to determine the authors of an unknown set of documents. The trained text-classification model is then used to identify the right author for a given text document.
Learn the basic technique to translate human-readable text to machine-readable format, and how to use attention mechanisms to improve results – especially for long strings.
Closing Comments and Questions
Quick overview of the next steps you could leverage to build and deploy your own applications
Basic experience with neural networks and Python programming, familiarity with linguistics.