Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling

Neural Networks    |    Intermediate
  • 9 videos | 36m 1s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 10 users Rating 4.2 of 10 users (10)
Explore artificial neural networks (ANNs), their essential components, tools, and frameworks for their implementation in machine learning solutions. In this 9-video course, you will discover recurrent neural networks (RNNs) and how they are implemented. Key concepts covered here include perceptrons and the computational role they play in ANNs; learning features and characteristics of ANNs and how components are used to build a model; and learning prominent tools and frameworks used to implement sequence models and ANNs. Next, you will learn about sequence modeling as it pertains to language models; RNNs and their capabilities and components; and how to specify RNN types and their implementation features. Learners will then explore the concept of linear and nonlinear functions and classify how they are used with perceptrons; explore the concept of backpropagation and usage of backpropagation algorithm in neural networks; and examine the concept of activation functions and how linear and nonlinear activations are utilized in neural networks. Finally, you will see how to implement perceptrons with Python, and how to use modeling tools and architectures and applications of sequence models.

WHAT YOU WILL LEARN

  • Describe artificial neural networks (anns) and their features and characteristics
    List artificial neural network components used to build a model
    List prominent tools and frameworks used implement sequence models and artificial neural networks
    Describe sequence modeling as it pertains to language models
  • Describe recurrent neural networks and their capabilities and components
    Specify rnn types and their implementation features
    Build a recurrent neural network using pytorch and google colab
    Recall ann characteristics, modeling tools, and architectures and applications of sequence models

IN THIS COURSE

  • 1m 38s
  • 3m 52s
    Upon completion of this video, you will be able to describe artificial neural networks (ANNs), their features, and characteristics. FREE ACCESS
  • Locked
    3.  Components of ANN
    4m 58s
    After completing this video, you will be able to list the components of an artificial neural network used to build a model. FREE ACCESS
  • Locked
    4.  Modeling Tools and Frameworks
    4m 38s
    Upon completion of this video, you will be able to list prominent tools and frameworks used to implement sequence models and artificial neural networks. FREE ACCESS
  • Locked
    5.  Sequence Modeling
    6m 12s
    Upon completion of this video, you will be able to describe sequence modeling as it pertains to language models. FREE ACCESS
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    6.  Recurrent Neural Network (RNN)
    3m 24s
    After completing this video, you will be able to describe recurrent neural networks, their capabilities, and components. FREE ACCESS
  • Locked
    7.  Types of RNN
    4m 10s
    Upon completion of this video, you will be able to specify RNN types and their implementation features. FREE ACCESS
  • Locked
    8.  Build a RNN with PyTorch and Google Colab
    5m 12s
    In this video, you will learn how to build a recurrent neural network using PyTorch and Google Colab. FREE ACCESS
  • Locked
    9.  Exercise: ANN and Sequence Modeling
    1m 58s
    Upon completion of this video, you will be able to recall characteristics of ANNs, modeling tools, and architectures and applications of sequence models. FREE ACCESS

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