Summary
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’ 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.
Program
Foundations of NLP
One-Hot Encoding
TF/IDF and Stemming
Stopwords
N-grams
Working with Word Embeddings
Neural Networks with Keras
Activation Functions
Loss Functions
Training procedures
Network Architectures
Text classification
Feed Forward Networks
Convolutional Neural Networks
Applications
Word Embeddings
Motivations
Skip-gram and Continuous Bag of words
Transfer Learning
Sequence Modeling
Recurrent Network Networks
Gated Recurrent Unit
Long-Short Term Memory
Encoder-Decoder Models
Text Generation