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Home » Technology » AI / Machine Learning » Deep Learning for Natural Language Processing

Course Skill Level:

Foundational

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    DLFNLPL21E09

Who should attend & recommended skills:

Those with Python experience and basic IT & Linux experience who want to use deep learning to learn NLP applications & solve NLP issues

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others who are intending to explore the most challenging NLP issues and learn how to solve them with deep learning! And to learn and build NLP applications, and know exactly what to look for when approaching new challenges.
  • Skill-level: Foundation-level Deep-Learning-for-Natural-Language-Processing skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Programming: Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

Deep Learning for Natural Language Processing teaches you to apply state-of-the-art deep learning approaches to natural language processing tasks. You will learn key NLP concepts like neural word embeddings, auto-encoders, part-of-speech tagging, parsing, and semantic inference. Then you will dive deeper into advanced topics including deep memory-based NLP, linguistic structure, and hyperparameters for deep NLP. Along the way, you will pick up emerging best practices and gain hands-on experience with a myriad of examples, all written in Python and the powerful Keras library. By the time you are done reading this invaluable course, you will be solving a wide variety of NLP problems with cutting-edge deep learning techniques!

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Natural Language Processing expert instructor, students will learn about and explore:
  • The most challenging NLP issues and learn how to solve them with deep learning!
  • A goldmine of unstructured textual data already exists, largely untapped simply because it does not follow any predefined format
  • NLP is poised to conquer that data with its impressive abilities to scan for keywords and phrases and discern sentiment and preferences
  • An overview of NLP and deep learning
  • One-hot text representations
  • Word embeddings
  • Models for textual similarity
  • Sequential NLP
  • Semantic role labeling
  • Deep memory-based NLP
  • Linguistic structure
  • Hyperparameters for deep NLP

Course breakdown / modules

  • Overview of the course
  • A selection of machine learning methods for NLP
  • Deep Learning
  • Vector representations of language
  • Vector sanitization
  • Wrapping up

  • Basic architectures of deep learning
  • Deep learning and NLP: a new paradigm
  • Wrapping up

  • Embeddings
  • From words to vectors: word2vec
  • From documents to vectors: doc2vec
  • Wrapping up
  • External resources

  • The problem
  • The data
  • Data representation
  • Models for measuring similarity
  • Authorship attribution
  • Authorship verification
  • Wrap up

  • Memory and language
  • Data and data processing
  • Question Answering with sequential models
  • Data and software resources

  • Memory networks for sequential NLP
  • Data and data processing
  • Strongly supervised memory networks: experiments and results
  • Semi-supervised memory networks
  • Semi-supervised memory networks: experiments and results
  • Code and data

  • Neural attention
  • Data
  • Static attention: MLP
  • Temporal attention: LSTM

  • Introduction
  • Data
  • Consumer reviews: Yelp and Amazon
  • Reuters topic classification
  • Part-of-speech and named entity recognition data

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