Workshop Natural Language Processing using Deep Learning​

NLP applications

Word vectors

• What are vectors?
• Word analogies
• TF-IDF and t-SNE
• NLTK
• GloVe
• Word2vec
• Text classification using word vectors

Hands-on Lab:

• Performing a basic text classification using multiple word vectors models
• Improve it by using basic text processing and language models to get the data ready for machine learning

Language modeling

• Bigrams
• Language models
• Neural Network Bigram Model

Hands-on Lab:

• Performing text classification using neural networks based on language models
• Understand the probabilistic modeling of language model, how to improve the context of a word and how synonyms can be generated and how basic neural networks generate powerful language models

Word Embeddings

• CBOW
• Skip-Gram
• Negative Sampling

Hands-on Lab: Understand advanced techniques for language modeling like Skip-Gram and Negative Sampling by implementing them and learn to predict the next most likely word in a conversation

NLP techniques

• What is POS Tagging?
• POS Tagging Recurrent Neural Network
• POS Tagging Hidden Markov Model (HMM)
• Named Entity Recognition (NER)
• POS vs. NER

Hands-on Lab: Use NLTK and SCIPY to improve your classification using grammar rules and POS, then use NER to highlight the most valuable content of a phrase, afterwards implement summarization

Recurrent Neural Networks

• LSTM
• GRU
• Text Generation

Hands-on Lab:

• Implement in Keras a basic RNN architecture for word prediction, using the already studied word embeddings
• Benchmark the performances of LSTM compared to GRU and BiLSTM

Generative Neural Networks

Hands-on Lab:

• Implement in Keras your own generative model that generates lyrics similar to the ones from Shakespeare
• Learn to make Transfer Learning on text