NLP with Deep Learning for Everyone
Published by Pearson
Implementing Text Classification, Sequence to Sequence Models and Word Embedding Approaches with Keras
Natural Language lies at the heart of current developments in Artificial Intelligence, User Interaction and Information Processing. The combination of unprecedented corpora of written text provided by Social Media and the massification of computational power has led to increased interest in the development of modern NLP tools based on state-of-the-art Deep Learning tools.
In this lecture, we will introduce participants to the fundamental concepts and algorithms used for Natural Language Processing through an in-depth exploration of different examples built using the Keras Python framework for Deep Learning. Applications to real datasets will be explored in detail.
What you’ll learn and how you can apply it
- Text representation
- Sentiment analysis
- Text Generation
- Text classification
- Word Embeddings
- Neural Networks
- Deep Networks
This live event is for you because...
- You need to learn how to process text data
- You want to understand how Keras can be used for NLP
- You want to apply deep learning approaches to text processing, understanding and generation
Prerequisites
- Python
- Basic Neural Networks
Course Set-up
- Python
- Pandas
- Keras
- Tensorflow
Recommended Preparation
- Live Online Training: Natural Language Processing (NLP) For Everyone by Bruno Gonçalves on the O'Reilly Learning Platform
- Live Online Training: Deep Learning For Everyone by Bruno Gonçalves on the O'Reilly Learning Platform
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Foundations of NLP Length: 50 mins
- One-Hot Encoding
- TF/IDF and Stemming
- Stopwords
- N-grams
- Working with Word Embeddings
Break (length: 10 mins)
Segment 2: Neural Networks with Keras Length: 60 mins
- Activation Functions
- Loss Functions
- Training procedures
- Network Architectures
Break (length: 10 mins)
Segment 3: Text classification Length: 30 mins
- Feed Forward Networks
- Convolutional Neural Networks
- Applications
Break (length: 5 mins)
Segment: 4 Word Embeddings Length: 30 mins
- Motivations
- Skip-gram and Continuous Bag of words
- Transfer Learning
Break (length: 5 mins)
Segment 5: Sequence Modeling Length: 40 mins
- Recurrent Network Networks
- Gated Recurrent Unit
- Long-Short Term Memory
- Encoder-Decoder Models
- Text Generation
Your Instructor
Bruno Gonçalves
Bruno Gonçalves is currently a Head of Data Science working at the intersection of AI, Blockchain Technologies, and Finance. Previously, he was a Data Science Fellow at NYU's Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since the completion of his PhD in the Physics of Complex Systems in 2008, he has pursued the use of Data Science and Machine Learning to the large-scale study of human behavior. In 2015, he was awarded the Complex Systems Society's Junior Scientific Award for "outstanding contributions in Complex Systems Science," and in 2018 he was named a Science Fellow of the Institute for Scientific Interchange in Turin, Italy.