Introduction to Natural Language Processing (NLP) – Deep Learning

Course Objectives

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course.  Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern deep learning experience into every classroom and hands-on project. In this course Students will explore:

Hands-on Projects

• Neural Text Classification. Develop a deep learning model to classify the sentiment of movie reviews as either positive or negative.
• Neural Language Modeling. Develop a neural language model on the text of Plato in order to generate new tracts of text with the same style and flavor as the original.
• Neural Photo Captioning. Develop a model to automatically generate a concise description of ad hoc photographs.
• Neural Machine Translation. Develop a model to translate sentences of text in German to English.

Neural Network Models

• Neural Bag-of-Words. Develop neural network models that model text as a bag-of-words where word order is ignored.
• Neural Word Embedding. Develop neural network models that model text using a distributed representation.
• Embedding + CNN. Develop deep learning models that combine word embedding representations with convolutional neural networks.
• Encoder-Decoder RNN. Develop recurrent neural networks that use the encoder-decoder architecture.

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate.  We offer additional related Machine Learning, AI, Deep Learning, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This in an intermediate and beyond-level course is geared for experienced developers or others (with prior Python experience) intending to start using learning about and working with Natural Language Processing soon after they attend this course. Attendees should be experienced developers who are comfortable with Python programming.  Students should also be able to navigate Linux command line, and who have basic knowledge of Linux editors (such as VI / nano) for editing code

Students should have attended or have incoming skills equivalent to those in this course:

• Strong basic Python Skills and basic deep learning knowledge.
• Prior working experience with Keras is also useful

Take Before: We recommend attendees have the skills in the course(s) listed below, or attend them as a pre-requisite:

• TTPS4873 Python for Data Science
• TTML6604 Introduction to Machine Learning & Deep Learning | Working with Mathematical Concepts, Algorithms, Deep Learning & More

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses or Follow On training options. Our team will be happy to help you with recommendations for next steps in your Learning Journey.

Course Agenda

1. Foundations

• Natural Language Processing
• Deep Learning
• Promise of Deep Learning for Natural Language
• How to Develop Deep Learning Models With Keras

2. Data Preparation

• How to Clean Text Manually and with NLTK
• How to Prepare Text Data with scikit-learn
• How to Prepare Text Data With Keras

3. Bag-of-Words

• The Bag-of-Words Model
• Prepare Movie Review Data for Sentiment Analysis
• Neural Bag-of-Words Model for Sentiment Analysis

4. Word Embeddings

• The Word Embedding Model
• How to Develop Word Embeddings with Gensim
• How to Learn and Load Word Embeddings in Keras

5. Text Classification

• Neural Models for Document Classification
• Develop an Embedding + CNN Model
• Develop an n-gram CNN Model for Sentiment Analysis

6. Language Modeling

• Neural Language Modeling
• Develop a Character-Based Neural Language Model
• How to Develop a Word-Based Neural Language Model
• Develop a Neural Language Model for Text Generation

7. Image Captioning

• Neural Image Caption Generation
• Neural Network Models for Caption Generation
• Load and Use a Pre-Trained Object Recognition Model
• How to Evaluate Generated Text With the BLEU Score
• How to Prepare a Photo Caption Dataset For Modeling
• Develop a Neural Image Caption Generation Model

8. Neural Machine Translation

• Neural Machine Translation
• Encoder-Decoder Models for NMT
• Configure Encoder-Decoder Models for NMT
• How to Develop a Neural Machine Translation Model

Course Materials

Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options