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Building Domain Specific Language Models

  • Course Code: Data Science - Building Domain Specific Language Models
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 1 Days Audience: This course is geared for those who wants to build the foundations of any domain-specific NLP system by creating the most a robust and efficient language model.

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level Building Domain Specific Language Models skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to build the foundations of any domain-specific NLP system by creating the most a robust and efficient language model. 
  • 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. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In this course, you will be taking on the role of an NLP data scientist at Stack Exchange, a network of question-and-answer (Q&A) websites on topics in diverse fields. Stack Exchange has over 10M registered users and is best known for its flagship websites Stack Overflow or Ask Ubuntu. You will build statistics-focused language models using gradually more complex methods. You will evaluate and apply these models to the tasks of: 

  • Query completion 
  • Larger text generation 
  • Sentence selection 

At the end of this course, you will be able to build the foundations of any domain-specific NLP system by creating the most a robust and efficient language model. 

Working in a hands-on learning environment, led by our Building Domain Specific Language Models expert instructor, students will learn about and explore: 

  • Starting with building n-gram language models, which will serve as a baseline for performance evaluations, 
  • moving on to a more complex modeling technique based on RNNs, 
  • finally, using state-of-the-art language model building with the AllenNLP framework.  The AllenNLP framework helps you design and evaluate deep-learning models for nearly any NLP problem. 

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

  • Loading and preparing the dataset 
  • Building and evaluating n-gram word-based language models 
  • Building a word-based language model using recurrent neural networks (RNNs) and word embeddings 
  • Building a character-based language model with AllenNLP 

Audience & Pre-Requisites 

This course is for proficient Python programmers who have experience with text-based machine learning. This course uses Python 3.7. It is recommended that you use the Anaconda distribution of Python and conda for managing the libraries.  

Pre-Requisites:  Students should have familiar with: 

TOOLS 

  • Basics of NumPy 
  • Basics of panda’s course 
  • Intermediate NLTK 
  • Basics of creating neural networks with PyTorch, TensorFlow, or Keras 

TECHNIQUES 

  • Basics of NumPy 
  • Basics of pandas 
  • Intermediate NLTK 
  • Basics of creating neural networks with PyTorch, TensorFlow, or Keras 

Course Agenda / Topics 

  1. Loading and Preparing the Dataset 
  • Loading and Preparing the Dataset 
  • Regular Expressions 
  • Tokenization 
  • Submit Your Work 
  1. N-gram Language Model 
  • N-gram Language Model 
  • Building Your Vocabulary with a Tokenizer 
  • Submit Your Work 
  1. Deep Learning Language Model 
  • Deep Learning Language Model 
  • Deep Learning for Text and Sequences 
  • Sequential NLP and Memory 
  • Submit Your Work 
  1. Character-based Language Model with AllenNLP 
  • Character-based Language Model with AllenNLP 
  • Sequential Labeling and Language Modeling 
  • Submit Your Work 
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