Convolutional and Recurrent Neural Networks

Machine Learning    |    Beginner
  • 13 videos | 33m 38s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 45 users Rating 4.2 of 45 users (45)
Some tasks aren't suitable for traditional neural networks and require specialized neural networks. Explore convolutional and recurrent neural networks and the types of problems they can solve.

WHAT YOU WILL LEARN

  • Describe convolutional neural networks, how they are different from regular neural networks, and how they are used
    Describe the high-level architecture of convolutional neural networks
    Describe how convolution layers are set in convolutional neural networks
    Describe how pooling layers work in convolutional neural networks
    Describe some training considerations for convolutional neural networks and how training can differ from traditional neural networks
    Describe regularization and how it applies to convolutional neural networks
    Describe regularization methods for convolutional neural networks
  • Describe implementing and training convolutional neural networks
    Describe recurrent neural networks, how they are different from regular neural networks, and how they are used
    Describe different types of recurrent neural networks
    Describe lstms networks in tensorflow
    Describe rnns and lstm for language modeling
    Use tensorflow to create a cnn that classifies images

IN THIS COURSE

  • 4m 3s
    After completing this video, you will be able to describe convolutional neural networks, how they differ from regular neural networks, and how they are used. FREE ACCESS
  • 2m 43s
    After completing this video, you will be able to describe the high-level architecture of convolutional neural networks. FREE ACCESS
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    3.  Convolution Layers
    1m 40s
    After completing this video, you will be able to describe how convolution layers are set up in convolutional neural networks. FREE ACCESS
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    4.  Pooling Layer
    1m 42s
    Upon completion of this video, you will be able to describe how pooling layers work in convolutional neural networks. FREE ACCESS
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    5.  CNN Training Considerations
    2m 22s
    Upon completion of this video, you will be able to describe some training considerations for convolutional neural networks and how training can differ from traditional neural networks. FREE ACCESS
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    6.  Regularization
    1m 59s
    After completing this video, you will be able to describe regularization and how it applies to convolutional neural networks. FREE ACCESS
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    7.  Regularization Methods for CNNs
    1m 48s
    After completing this video, you will be able to describe regularization methods for convolutional neural networks. FREE ACCESS
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    8.  Convolutional Neural Network Implementation
    2m 16s
    After completing this video, you will be able to describe how to implement and train convolutional neural networks. FREE ACCESS
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    9.  Introducing Recurrent Neural Networks (RNNs)
    2m 13s
    Upon completion of this video, you will be able to describe recurrent neural networks, how they differ from regular neural networks, and how they are used. FREE ACCESS
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    10.  RNN Types
    1m 51s
    Upon completion of this video, you will be able to describe different types of recurrent neural networks. FREE ACCESS
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    11.  Long Short Term Memory (LSTMs) in TensorFlow
    1m 56s
    After completing this video, you will be able to describe Long Short-Term Memory networks in TensorFlow. FREE ACCESS
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    12.  RNNs and LSTM for Language Modeling
    6m 53s
    Upon completion of this video, you will be able to describe RNNs and LSTM for language modeling. FREE ACCESS
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    13.  Exercise: Image Classification
    2m 11s
    In this video, you will learn how to use TensorFlow to create a CNN that classifies images. FREE ACCESS

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