# Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling

Neural Networks    |    Intermediate
• 9 videos | 36m 1s
• Includes Assessment
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

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

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