NLP and Deep Learning
In this course you will learn about deep neural networks, and how to use them in processing text with Python (Natural Language Processing or NLP).
Overview
In this course you will learn about deep neural networks (deep learning), and how to leverage them in processing, understanding and mining for insights from text. We start with an introduction to neural networks and deep learning. We then dive into essentials of representation learning like word and document embeddings and then move onto more complex methodologies including convolutional neural networks and sequence models and deep transfer learning approaches including universal embeddings and transformers. Popular applications are also covered with hands-on tutorials and exercises including text classification, information extraction, recommenders, search, summarization, translation and more.
- Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
In this course you will learn:
- How to specify and run artificial neural networks and deep networks
- How deep networks represent words as binary vectors
- How to use recurrent neural networks for sequential learning (sequence-to-sequence modeling)
- How to use attention models to improve predictive performance
Who Should Take This Course
Data scientists and aspiring data scientists.
Our Instructors
Mr. Dipanjan Sarkar
Dipanjan (DJ) Sarkar is a Data Science Lead, published author and has been recognized as a Google Developer Expert in Machine Learning by Google in 2019. He has also been recognized as one of the Top Ten Data Scientists in India, 2020 by a few leading technology magazines and publishing houses. Dipanjan has led advanced analytics initiatives working with several Fortune 500 companies like Applied Materials, Intel and Open Source organizations like Red Hat (now IBM). He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems.
He holds a master of technology degree from IIIT Bangalore, with specializations in data science and software engineering and completed his post graduate diploma in machine learning and artificial intelligence from Columbia University in the City of New York.
Dipanjan has been an analytics practitioner and consultant for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Advisor, Subject Matter Expert and Instructor at various organizations like Springboard, Propulsion Academy and Statistics.com (The Institute for Statistics Education) where he helps people build their skills on areas in data science and artificial intelligence. Dipanjan also beta-tests new courses on data science for popular MOOC platform, Coursera, before they are released. He is a published author, having authored several books on R, Python, Machine Learning, Natural Language Processing, and Deep Learning which includes Text Analytics with Python 2nd ed.
Course Syllabus
Week 1
Introduction to Deep Learning and Representation Learning
- Introduction to deep neural networks and deep learning
- Applications of Deep Learning for NLP
- Essential components of deep learning
- Types of models (especially for NLP)
- Layers, activation and loss functions
- Gradient descent and Backpropagation
- Representation Learning for Text Data
- Understanding Embeddings
- Types of Embeddings
- CBOW (Continuous Bag of Words) and SkipGram
- Word Embedding Models – Word2Vec, GloVe, FastText
- Obtaining document embeddings
Week 2
Context Sensitive Learning: Convnets and Sequential Models
- Convolutional Neural NetworksConvnets for NLP
- Convolutional Neural Networks
- 1D-CNNs for Text Classification
- Sequential Models for NLP
- Recurrent Neural Networks
- Long Short Term Memory Networks (LSTMs)
- Gated Recurrent Units (GRUs)
- Bi-directional LSTMs (Bi-LSTMs)
- Contextual Embeddings using BiLSTMs – ELMO
- Sequence-to-Sequence Models for NLP
- Encoder-Decoder Models
- Applications of Encoder-Decoder Models
Week 3
Deep Transfer Learning for NLP
- Transfer Learning
- Need and importance
- Methodologies
- Pre-trained word embeddings
- Word2vec, GloVe, FastText
- Universal Embeddings
- Neural net language model (NNLM)
- Universal Sentence Encoder
- Transformers and Multi-task Learning
- Brief and types of transformers
- Advantages of pre-trained transformers
- Applications
Week 4
Attention, Transformers and Applications
- Limitations of Sequential and Sequence-to-sequence models
- Attention in sequential Models
- Understanding Transformer Models
- Encoders and Decoders
- Self Attention and Multi-headed Attention
- Contextual and Pooled Embeddings
- BERT
- Transformers Variants
- Bidirectional Encoder Representations from Transformers (BERT) variants – Roberta, Albert, XLNet etc.
- Efficient Transformers with knowledge distillation – DistilBERT
- Generative Transformers – generative pre-training (GPT) family
Class Dates
2022
Instructors:
2023
Instructors:
Instructors:
Instructors:
2024
Instructors:
Prerequisites
You should be sufficiently familiar with Python to follow and use code examples (see suggested course below). You should also be familiar with the neural net material covered in the predictive analytics course, below.
Introduction to NLP and Text Mining
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
Predictive Analytics 2 with Python – Neural Nets and Regression
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
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Additional Information
Homework
Homework in this course consists of coding exercises using Python.
Course Text
The text used for the practical work in this course is Text Analytics with Python (Apress, 2019) by Dipanjan Sarkar, chosen for its wealth of hands on Python illustrations and code. The code for these illustrations is organized here:
https://github.com/dipanjanS/text-analytics-with-python/tree/master/New-Second-Edition
Note: this text is also used in the introductory course, Introduction to NLP and Text Mining
Software
The course uses Python.
Options for Credit and Recognition
ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 2 semester hours in computer science, computer information systems, or cyber security. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.
Supplemental Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
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- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
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- Other native accessibility features instructions
Safari
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