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:
- Classical approaches to convert text to a machine-understandable representation.
- Implementation and properties of distributed representations embeddings.
- Methods to train machine translators from one language to another.
Why Deep Learning Institute Hands-On Training?
- Learn how to build deep learning and accelerated computing applications across a wide range of industry segments such as autonomous vehicles, digital content creation, finance, game development, and healthcare
- Obtain guided hands-on experience using the most widely-used, industry-standard software, tools, and frameworks
- Gain real-world expertise through content designed in collaboration with industry leaders including the Children’s Hospital Los Angeles, Mayo Clinic, and PwC
- Earn NVIDIA DLI Certification to demonstrate your subject matter competency and support professional career growth
- Access content anywhere, anytime with a fully-configured, GPU-accelerated workstation in the cloud
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
- Importance of data representation for computers to understand language
Overview of NLP challenges and how to tackle them with deep learning
- Overview of word2vec algorithm for text classification
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.
- Build a linguistic style model to extract features from a given set of texts using embeddings
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.
- Create a neural machine translation model to convert text from one language to another
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
- Wrap-up, potential next steps, and Q&A
Quick overview of the next steps you could leverage to build and deploy your own applications
- Tools, libraries, and frameworks: TensorFlow, Keras
Basic experience with neural networks and Python programming, familiarity with linguistics.