Deep Learning for Natural Language Processing: Applications of Deep Neural Networks to Machine Learning Tasks LiveLessons

Pearson presents Deep Learning for Natural Language Processing: Applications of Deep Neural Networks to Machine Learning Tasks.

Deep Learning for Natural Language Processing: Applications of Deep Neural Networks to Machine Learning Tasks LiveLessons

Course Description

An intuitive introduction to processing natural language data with Deep Learning models Deep Learning for Natural Language Processing LiveLessons is an introduction to processing natural language with Deep Learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons from this Pearson course, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the-art Deep Learning architectures are leveraged to make predictions with natural language data. What you will learn:... Read More »

An intuitive introduction to processing natural language data with Deep Learning models Deep Learning for Natural Language Processing LiveLessons is an introduction to processing natural language with Deep Learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons from this Pearson course, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the-art Deep Learning architectures are leveraged to make predictions with natural language data.

What you will learn:

  • Preprocess natural language data for use in machine learning applications
  • Transform natural language into numerical representations with word2vec
  • Make predictions with Deep Learning models trained on natural language
  • Apply state-of-the-art NLP approaches with Keras, the high-level TensorFlow API
  • Improve Deep Learning model performance by tuning hyperparameters
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Course Details:

Target Audience

  • These LiveLessons are perfectly-suited to software engineers, data scientists, analysts, and statisticians with an interest in applying Deep Learning to natural language data. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.
  • Prerequisites

  • The author’s earlier Deep Learning with TensorFlow LiveLessons, or equivalent foundational Deep Learning knowledge, are a prerequisite.
  • Certificate Info:

    Type of Certification

    Certificate of Completion

    Format of Certification

    Digital and Print

    Professional Association/Affiliation

    This certificate is issued by Pearson LearnIT

    Method of Obtaining Certification

    Upon successful completion of the course, participants will receive a certificate of completion.

    Course Outline

  • Deep Learning for Natural Language Processing: Introduction
  • Topics
  • Introduction to Deep Learning for Natural Language Processing
  • Computational Representations of Natural Language Elements
  • NLP Applications
  • Installation, Including GPU Considerations
  • Review of Prerequisite Deep Learning Theory
  • A Sneak Peak
  • Topics
  • Vector-Space Embedding
  • word2vec
  • Data Sets for NLP
  • Creating Word Vectors with word2vec
  • Topics
  • Best Practices for Preprocessing Natural Language Data
  • The Area Under the ROC Curve
  • Dense Neural Network Classification
  • Convolutional Neural Network Classification
  • Topics
  • Essential Theory of RNNs
  • RNNs in Practice
  • Essential Theory of LSTMs and GRUs
  • LSTMs and GRUs in Practice
  • Topics
  • Bi-Directional LSTMs
  • Stacked LSTMs
  • Parallel Network Architectures
  • Hyperparameter Tuning
  • Deep Learning for Natural Language Processing: Summary
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