Description
This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.
• Predict future values of a time-series
• Classify free form text
• Address time-series and text problems with recurrent neural networks
• Choose between RNNs/LSTMs and simpler models
• Train and reuse word embeddings in text problems
You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together.
Prerequisites: Basic SQL, familiarity with Python and TensorFlow
What you will learn
Course introduction
This module addresses the reasons to learn NLP from Google and provides an overview of the course structure and goals.
NLP on Google Cloud
This module introduces the NLP architecture on Google Cloud. It explores the NLP history, the NLP APIs such as the Dialogflow API, and the NLP solutions such as Contact Center AI and Document AI.
NLP with Vertex AI
This module explores AutoML and custom training, which are the two options to develop an NLP project with Vertex AI. Additionally, the module introduces an end-to-end NLP workflow and provides a hands-on lab to apply the workflow to solve a task of text classification with AutoML.
Text representatation
This module describes the process to prepare text data in NLP and introduces the major categories of text representation techniques.
NLP models
This module describes different NLP models including ANN, DNN, RNN, LSTM, and GRU. It also introduces the benefits and disadvantages of each model.
