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Deep Learning for Natural Language Processing

  • Course Code: Artificial Intelligence - Deep-Learning-for-Natural-Language-Processing
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: 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!

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Deep-Learning-for-Natural-Language-Processing skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: 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! 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Deep Learning for Natural Language Processing teaches you to apply state-of-the-art deep learning approaches to natural language processing tasks. You’ll learn key NLP concepts like neural word embeddings, auto-encoders, part-of-speech tagging, parsing, and semantic inference. Then you’ll dive deeper into advanced topics including deep memory-based NLP, linguistic structure, and hyperparameters for deep NLP. Along the way, you’ll 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’re done reading this invaluable course, you’ll be solving a wide variety of NLP problems with cutting-edge deep learning techniques! 

Working in a hands-on learning environment, led by our Natural Language Processing expert instructor, students will learn about and explore: 

  • 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 doesn’t 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. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • 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 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to learn and build NLP applications, and know exactly what to look for when approaching new challenges. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Deep learning for NLP 
  • Overview of the course 
  • A selection of machine learning methods for NLP 
  • Deep Learning 
  • Vector representations of language 
  • Vector sanitization 
  • Wrapping up 
  1. Deep learning and language: the basics 
  • Basic architectures of deep learning 
  • Deep learning and NLP: a new paradigm 
  • Wrapping up 
  1. Text embeddings 
  • Embeddings 
  • From words to vectors: word2vec 
  • From documents to vectors: doc2vec 
  • Wrapping up 
  • External resources 
  1. Textual similarity 
  • The problem 
  • The data 
  • Data representation 
  • Models for measuring similarity 
  • Authorship attribution 
  • Authorship verification 
  • Wrap up 
  1. Sequential NLP and memory 
  • Memory and language 
  • Data and data processing 
  • Question Answering with sequential models 
  • Data and software resources 
  1. Episodic memory for NLP 
  • 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 
  1. Attention 
  • Neural attention 
  • Data 
  • Static attention: MLP 
  • Temporal attention: LSTM 
  1. Multitask learning 
  • Introduction 
  • Data 
  • Consumer reviews: Yelp and Amazon 
  • Reuters topic classification 
  • Part-of-speech and named entity recognition data 
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