Applied Deep Learning: Unsupervised Data

Machine Learning
  • 11 Videos | 1h 32m 25s
  • Includes Assessment
  • Earns a Badge
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This 11-video course explores the concept of deep learning and implementation of deep learning-based frameworks for natural language processing (NLP) and audio data analysis. Discover the architectures of recurrent neural network (RNN) that can be used in modeling NLP, and the challenges of unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature machine learning. First, examine the prominent statistical classification models and compare generative classifiers with discriminative classifiers; then recall different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model. Learn about setting up and working with PixelCNN; explore differences between multilayer perception (MLP), convolutional neural network (CNN), and RNN. Explore the essential capabilities and variants of ResNet that can be used for computer vision and deep learning. Finally, take a look at encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders. The concluding exercise involves recalling RNN architecture that can be used in modeling NLP, variants of ResNet, and setting up PixelCNN.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recall the concept of deep learning and the approach of using deep learning-based frameworks to model NLP tasks and audio data analysis
    describe the role of recurrent neural network and the various architectures of recurrent neural network that can be used in modeling natural language processing
    recognize the challenges associated with unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature learning
    describe the prominent statistical classification models and compare generative classifiers with discriminative classifiers
    recall the different types of generative models, with focus on generative adversarial network, variational autoencoders, and flow-based generative model
  • demonstrate the steps involved in setting up and working with PixelCNN
    describe the characteristics of the different classes of artificial neural networks and the difference between MLP, CNN, and RNN
    recognize the essential capabilities and variants of ResNet that can be used for computer vision and deep learning
    describe encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders
    recall the prominent architectures of recurrent neural network that can be used in modeling natural language processing, list the variants of ResNet that can be used for computer vision and deep learning, and set up PixelCNN

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 22s
    UP NEXT
  • Playable
    2. 
    Deep Learning to Model NLP and Audio Analysis
    13m 10s
  • Locked
    3. 
    Recurrent Neural Network Architectures
    14m 9s
  • Locked
    4. 
    Unsupervised Learning Challenges in Deep Learning
    7m 47s
  • Locked
    5. 
    Generative and Discriminative Classifiers
    4m 13s
  • Locked
    6. 
    Types of Generative Models
    4m 17s
  • Locked
    7. 
    PixelCNN Setup
    17m 33s
  • Locked
    8. 
    Differences between MLP, CNN, and RNN
    6m 9s
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    9. 
    ResNet for Computer Vision
    6m 17s
  • Locked
    10. 
    Encoders and Autoencoders
    7m 10s
  • Locked
    11. 
    Exercise: RNN and ResNet
    5m 49s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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