Discover the fundamentals and advanced applications of natural language processing using deep learning. The Natural Language Processing using Deep Learning course introduces you to modern techniques such as word embeddings, recurrent neural networks, and generative models, giving you practical skills for text classification and analysis.
This course is aimed at Machine Learning Engineers.
In this course, students will learn about the most popular architectures, including Recurrent Natural Networks and Hidden Markov Models.
In order to participate in this course, students must have completed the Basic Machine Learning in Tensorflow/Keras module.
It is useful for participants to have the following knowledge:
Basic Deep Learning
● Neurons
● Types of Layers
● Networks
● Loss Functions
● Optimizers
● Overfitting
● Tensorflow
Basic Neural Language Processing
● Tokenization
● Bag of words
● tf-idf
● Stemming
● Lemmatization
● Language models
● Sentiment analysis
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. NO
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
- CRANE
- 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.
Natural Language Processing using Deep Learning
