Natural Language Processing using Deep Learning

Module 1: NLP applications

Module 2: 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

Module 3: 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

Module 4: 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

Module 5: 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

Module 6: 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

Module 7: 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