Business Intelligence Course

course overview

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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.

Skills Gained

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
  • 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.


Basic experience with neural networks and Python programming, familiarity with linguistics.


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

Word Embeddings

  • 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.

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.

Text Translation

  • 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

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Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

All $ prices are in USD unless it’s a NZ or AU date

SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

Please Note: All courses are availaible as Live Virtual Classes

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Our clients have included prestigious national organisations such as Oxford University Press, multi-national private corporations such as JP Morgan and HSBC, as well as public sector institutions such as the Department of Defence and the Department of Health.