Deep Learning for Natural Language Processing
Description
Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the
natural language processing domain. The course goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning course, you’ll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM).
Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search.
By the end of this course, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues.
Course Objectives
Understand various pre-processing techniques for deep learning problems
• Build a vector representation of text using word2vec and GloVe
• Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP
• Build a machine translation model in Keras
• Develop a text generation application using LSTM
• Build a trigger word detection application using an attention model
Garis Besar Pelatihan
• Basics of Natural Language Processing & application areas.
• Introduction to popular text pre-processing techniques.
• Introduction to word2vec and Glove word embeddings.
• Sentiment classification.
• Introduction to Named Entity Recognition.
• Introduction to Parts of Speech Tagging.
• Using popular libraries to develop a Named Entity Recognizer
• Introduction to Neural Networks.
• Basics of Gradient descent and backpropagation.
• What is Deep Learning.
• Introduction to Keras.
• Fundamentals of deploying a model as a service.
• Introduction to CNN.
• Understanding the architecture of a CNN.
• Application areas of a CNN.
• Implementation using Keras.
• Introduction to RNN.
• Understanding the architecture of a RNN.
• Application areas of a RNN.
• Implementation using Keras.
• Vanishing Gradients with RNN.
• Summarizing document using word frequency
• Generating random text using the markov chain
• Compare the results between recent methods
• Introduction to LSTM.
• Understanding the architecture of an LSTM.
• Application areas.
• Implementation using Keras.
• Attention Model & Beam search.
• End to End models for speech processing.
• Dynamic Neural Networks for question answering.
• Data acquisition (Free datasets, crowd-sourcing)
• Using cloud infrastructure to train deep learning NLP model (Google colab notebook)
• Writing a Flask framework server RestAPI to deploy a model
• Deploy the web service on cloud infrastructure (AWS ec2 instance, docker)
• Current promising techniques in NLP (BERT and others).
Target Audience
If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.
Scope
Deep Learning with NLP perfectly balances theory and exercises. Each module is designed to build on the learnings of the previous module. The course contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.
Deep Learning for Natural Language Processing
- 🖥️ Data Science
- 🔰 Level: Beginner - Intermediate
- 🌎 Bahasa: Indonesia
- 📜 Sertifikat: E-Sertifikat
- 💻 Akses: e-Learning
