Applied Deep Learning: Unsupervised Data

Machine Learning    |    Intermediate
  • 11 videos | 1h 27m 55s
  • Includes Assessment
  • Earns a Badge
Rating 4.1 of 12 users Rating 4.1 of 12 users (12)
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

  • 1m 22s
  • 13m 10s
    After completing this video, you will be able to recall the concept of deep learning and the approach of using deep learning-based frameworks to model NLP tasks and audio data analysis. FREE ACCESS
  • Locked
    3.  Recurrent Neural Network Architectures
    14m 9s
    After completing this video, you will be able to describe the role of recurrent neural networks and the various architectures of recurrent neural networks that can be used in modeling natural language processing. FREE ACCESS
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    4.  Unsupervised Learning Challenges in Deep Learning
    7m 47s
    Upon completion of this video, you will be able to recognize the challenges associated with unsupervised learning and the approach of using deep learning from the perspective of common unsupervised feature learning. FREE ACCESS
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    5.  Generative and Discriminative Classifiers
    4m 13s
    Upon completion of this video, you will be able to describe the prominent statistical classification models and compare generative classifiers with discriminative classifiers. FREE ACCESS
  • Locked
    6.  Types of Generative Models
    4m 17s
    After completing this video, you will be able to recall the different types of generative models, with a focus on generative adversarial networks, variational autoencoders, and flow-based generative models. FREE ACCESS
  • Locked
    7.  PixelCNN Setup
    17m 33s
    In this video, you will learn how to apply the steps involved in setting up and working with PixelCNN. FREE ACCESS
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    8.  Differences between MLP, CNN, and RNN
    6m 9s
    Upon completion of this video, you will be able to describe the characteristics of the different classes of artificial neural networks and the difference between MLP, CNN, and RNN. FREE ACCESS
  • Locked
    9.  ResNet for Computer Vision
    6m 17s
    Upon completion of this video, you will be able to recognize the essential capabilities and variants of ResNet that can be used for computer vision and deep learning. FREE ACCESS
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    10.  Encoders and Autoencoders
    7m 10s
    Upon completion of this video, you will be able to describe encoders in neural networks and compare the capabilities of standard autoencoders and variational autoencoders. FREE ACCESS
  • Locked
    11.  Exercise: RNN and ResNet
    5m 49s
    After completing this video, you will be able to 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. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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