6 students

Deep Learning in NLP

Asynchronous Online Course

  • Length: 10 weeks (20 hrs)

Important Notes: 

  • A “Certificate of Completion” will be awarded to you, if you pass the final exam.

Course Description

If you know the basic underlying of machine learning and you would like to learn how to run an AI project on time-series data, this is the right place for you to learn the state-of-the-art sequence models. More specifically, we discuss the natural language processing (NLP) in which data is a sequence of words/letters.

In the first part, we learn the basics of NLP and recurrent neural networks (RNNs) which are widely used in sequence modeling for time-series data. Besides a multi-label classification task in Lab 1 with Python, we experience a deep learning implementation for sequence tagging in Lab 2 with PyTorch DL framework. The second part also discusses attention mechanisms and Transformers, which are also state-of-the-art sequence modeling techniques. We learn how to use pre-trained models in a transfer learning fashion for new tasks with small supervised data.

Avoiding from the complex mathematics, this course comprehensibly presents the underlying concepts of the deep learning models for time-series data (RNNs) and their applications. Moreover, we will review attention models. The concepts are supported by Lab session(s) by introducing popular python libraries and implementing some samples.

Course Outline

Part 1

  1. Introduction (Definitions, Applications, Tasks, Approaches)
  2. Basics of Linguistics (NLP Pyramid, Vectorizations: BoW, TF-IDF, word2vec)
  3. Lab 1. Automatic Tagging in Python
  4. Recurrent Neural Networks (ML Components, LSTM, GRU)
  5. Lab 2. Named Entity Recognition in PyTorch

Part 2

  1. Attention mechanisms & Transformers
  2. BERT
  3. Lab 3. Spam classification with BERT in PyTorch
  4. Chatbots & Question/Answering
  5. Lab 4. Q/A with BERT in PyTorch

Prerequisites

  • It is highly suggested to complete the Introduction to AI & ML and Unsupervised Learning courses in advance.
  • Basic Python programming is needed. For the lab session, we use Google Colab. So, you don’t need to install anything in your machine. The DL programs employs PyTorch framework.

Resources

 

The AI & ML Series

DL for NLP with Python is the third course in the AI & ML series. List of courses in the AI & ML boot camp is as follows:

  1. Introduction to AI & ML
  2. Unsupervised Learning
  3. DL for NLP with Python
  4. Reinforcement Learning
  5. Convolutional Neural Networks

Instructor

Daryoush Mortazavi, PhD, PEng, is the founder and CEO of 'Synnovate Institute of Research & Education (SIRE)'

$45.00

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