Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling
Neural Networks
| Intermediate
- 9 Videos | 36m 1s
- Includes Assessment
- Earns a Badge
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
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describe artificial neural networks (ANNs) and their features and characteristicslist artificial neural network components used to build a modellist prominent tools and frameworks used implement sequence models and artificial neural networksdescribe sequence modeling as it pertains to language models
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describe recurrent neural networks and their capabilities and componentsspecify RNN types and their implementation featuresbuild a recurrent neural network using PyTorch and Google Colabrecall ANN characteristics, modeling tools, and architectures and applications of sequence models
IN THIS COURSE
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1.Course Overview1m 38sUP NEXT
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2.Artificial Neural Network (ANN)3m 52s
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3.Components of ANN4m 58s
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4.Modeling Tools and Frameworks4m 38s
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5.Sequence Modeling6m 12s
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6.Recurrent Neural Network (RNN)3m 24s
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7.Types of RNN4m 10s
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8.Build a RNN with PyTorch and Google Colab5m 12s
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9.Exercise: ANN and Sequence Modeling1m 58s
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