Natural Language Processing using Deep Learning course is suited for those who want to learn how to process and understand text. It covers the most popular architectures, including Recurrent Neural Networks and Hidden Markov Models.
It would be preferred to take the Basic Machine Learning course beforehand so the student is accustomed to the vocabulary and the main frameworks.
 
 
Esta formação é ministrada em Inglês.
Em parceria com a entidade acreditada:
Pré-Requisitos
Nice to have:
- Basic Deep Learning
- Neurons
- Types of Layers
- Networks
- Loss Functions
- Optimizers
- Overfitting
- Tensorflow
- Basic Natural Language Processing
- Tokenization
- Bag of words
- Tf-idf
- Stemming
- Lemmatization
- Language models
- Sentiment analysis
Programa
- NLP applications
- Word vectors
- Language modeling
- Word Embeddings
- NLP techniques
- Recurrent Neural Networks
- Generative Neural Networks
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
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
