Deep Learning for NLP: Introduction

Natural Language Processing    |    Intermediate
  • 14 Videos | 1h 17m 30s
  • Includes Assessment
  • Earns a Badge
In recent times, natural language processing (NLP) has seen many advancements, most of which are in deep learning models. NLP as a problem is very complicated, and deep learning models can handle that scale and complication with many different variations of neural network architecture. Deep learning also has a broad spectrum of frameworks that supports NLP problem solving out-of-the-box. Explore the basics of deep learning and different architectures for NLP-specific problems. Examine other use cases for deep learning NLP across industries. Learn about various tools and frameworks used such as - Spacy, TensorFlow, PyTorch, OpenNMT, etc. Investigate sentiment analysis and explore how to solve a problem using various deep learning steps and frameworks. Upon completing this course, you will be able to use the essential fundamentals of deep learning for NLP and outline its various industry use cases, frameworks, and fundamental sentiment analysis problems.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall basic concepts of natural language processing (NLP) with deep learning (DL)
    illustrate various use cases in NLP across different industries
    outline the basic concepts of spaCy and TensorFlow
    outline the basic concepts of Keras and PyTorch
    outline the basic concepts of Open Neural Machine Translation (OpenNMT) and DeepNL
    define basic concepts of sentiment data
  • explore the end-to-end components for a natural language processing (NLP) sentiment dataset
    illustrate the basics of data loading and columns
    demonstrate exploratory data analysis (EDA) of sentiment data
    demonstrate pre-processing and feature engineering of sentiment data
    demonstrate simple machine learning (ML) modeling, tuning, and evaluation using Keras
    demonstrate creating accuracy graphs and graphs for loss over time
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 21s
    UP NEXT
  • Playable
    2. 
    NLP with Deep Learning
    4m 26s
  • Locked
    3. 
    NLP Use Cases in Deep Learning
    5m 13s
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    4. 
    Basic Deep Learning Frameworks
    1m 40s
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    5. 
    Intermediate Deep Learning Frameworks
    2m 23s
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    6. 
    Advanced Deep Learning Frameworks
    2m 2s
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    7. 
    Introduction to Sentiment Data
    3m 41s
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    8. 
    Using Deep Learning Pipelines for Sentiment Data
    6m 15s
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    9. 
    Sentiment Analysis - Overview & Data
    6m 32s
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    10. 
    Sentiment Analysis - EDA
    14m 46s
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    11. 
    Sentiment Analysis - Pre-processing
    6m 52s
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    12. 
    Sentiment Analysis - Modeling & Evaluation
    12m 49s
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    13. 
    Sentiment Analysis - Creating Accuracy & Loss Graphs
    8m 26s
  • Locked
    14. 
    Course Summary
    1m 5s

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