Natural Language Processing in TensorFlow (Coursera)

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If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

Description

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input into a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow.

Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning.

This new deeplearning.ai Natural Language Processing in TensorFlow Coursera course teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

What you will learn

  • Build natural language processing systems using TensorFlow
  • Process text, including tokenization and representing sentences as vectors
  • Apply RNNs, GRUs, and LSTMs in TensorFlow
  • Train LSTMs on existing text to create original poetry and more

Syllabus – Natural Language Processing in TensorFlow Coursera Course

Sentiment in text

Word Embeddings

Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens and sequencing sentences from these tokens. This week you’ll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labeled examples, these vectors can then be tuned so that words with similar meanings will have a similar direction in the vector space. This will begin the process of training a neural network to understand the sentiment in the text — and you’ll begin by looking at movie reviews, training a neural network on texts that are labeled ‘positive’ or ‘negative’, and determining which words in a sentence drive those meanings.

Sequence models

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1 review for Natural Language Processing in TensorFlow (Coursera)

  1. 5 out of 5

    MOOC Course Editorial Staff

    nice course

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natural language processing in tensorflow coursera Course
Natural Language Processing in TensorFlow (Coursera)