Natural Language Processing Using Deep Learning

NLP    |    Intermediate
  • 14 videos | 1h 55m 5s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 1 users Rating 4.0 of 1 users (1)
Deep learning has revolutionized natural language processing (NLP), offering powerful techniques for understanding, generating, and processing human language. Through deep neural networks (DNNs), NLP models can now comprehend complex linguistic structures, extract meaningful information from vast amounts of text data, and even generate human-like responses. Begin this course by learning how to utilize Keras and TensorFlow to construct and train neural networks. Next, you will build a DNN to classify messages as spam or not. You will find out how to encode data using count vector and term frequency-inverse document frequency (TF-IDF) encodings via the Keras TextVectorization layer. To enhance the training process, you will employ Keras callbacks to gain insights into metrics tracking, TensorBoard integration, and model checkpointing. Finally, you will apply sentiment analysis using word embeddings, explore the use of pre-trained GloVe word vector embeddings, and incorporate convolutional layers to grasp local text context.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe deep learning with tensorflow
    Load and analyze text data
    Clean and visualize text data
    Generate count vector representations for text using the textvectorization layer
    Train a dnn model
    Generate term frequency-inverse document frequency (tf-idf) encodings for text
  • Train a dnn on tf-idf encodings
    View the tensorboard callback output
    Load and preprocess data for sentiment analysis
    Train a dnn on word embeddings
    Train a dnn on glove embeddings
    Train a cnn for sentiment analysis
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 8s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 7m 27s
    After completing this video, you will be able to describe deep learning with TensorFlow. FREE ACCESS
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    3.  Loading and Exploring a Text Dataset
    11m 30s
    During this video, you will learn how to load and analyze text data. FREE ACCESS
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    4.  Cleaning and Visualizing Data
    9m 48s
    In this video, find out how to clean and visualize text data. FREE ACCESS
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    5.  Generating Count Vector Representations
    8m 34s
    Discover how to generate count vector representations for text using the TextVectorization layer. FREE ACCESS
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    6.  Training a Deep Neural Network (DNN) Classification Model
    13m 4s
    Learn how to train a DNN model. FREE ACCESS
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    7.  TF-IDF Representations Using the TextVectorization Layer
    4m 53s
    In this video, discover how to generate term frequency-inverse document frequency (TF-IDF) encodings for text. FREE ACCESS
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    8.  Training a DNN Using TF-IDF Vectors
    8m 18s
    Find out how to train a DNN on TF-IDF encodings. FREE ACCESS
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    9.  Visualizing the Results of TensorFlow Callbacks
    8m 47s
    During this video, you will learn how to view the TensorBoard callback output. FREE ACCESS
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    10.  Loading and Preprocessing Data for Sentiment Analysis
    9m 35s
    Discover how to load and preprocess data for sentiment analysis. FREE ACCESS
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    11.  Training a DNN Using Word Embeddings
    10m 45s
    In this video, find out how to train a DNN on word embeddings. FREE ACCESS
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    12.  Training a DNN Using Pretrained GloVe Word Embeddings
    10m 55s
    Learn how to train a DNN on GloVe embeddings. FREE ACCESS
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    13.  Using a Convolutional Neural Network (CNN) for Sentiment Analysis
    6m 40s
    During this video, discover how to train a CNN for sentiment analysis. FREE ACCESS
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    14.  Course Summary
    2m 39s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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