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