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Artificial Intelligence Foundations: Neural Networks

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An artificial neural network uses the human brain as inspiration for creating a complex machine learning system. They can classify millions of sounds, videos, and images, answer our questions, understand our behaviors, and even drive our cars. Neural networks are also the foundation of generative AI.

This course introduces the fundamental techniques and principles of neural networks, common models, and their applications. Instructor Gwendolyn Stripling takes you through the different neural network architectures, their components, appropriate use cases, and best practices for improving neural network model performance. Plus, gain hands-on experience building and training a neural network using the Keras Sequential API, an open-source library that demystifies the design and training of neural networks. If you’re looking to achieve a solid understanding of how to build, train, improve and use neural networks, join Gwendolyn in this course.


01 - Introduction
  • 01 - Neural networks 101 Your path to AI brilliance
  • 02 - What you should know
  • 03 - How to use the challenge exercise files

  • 02 - 1. What Are Neural Networks
  • 01 - Machine learning and neural networks
  • 02 - Biological neural networks
  • 03 - Artificial neural networks
  • 04 - Single-layer perceptron

  • 03 - 2. Key Components in Neural Network Architecture
  • 01 - Multilayer perceptron
  • 02 - Layers Input, hidden, and output
  • 03 - Transfer and activation functions
  • 04 - How neural networks learn

  • 04 - 3. Other Types of Neural Networks
  • 01 - Convolutional neural networks (CNN)
  • 02 - Recurrent neural networks (RNN)
  • 03 - Transformer architecture

  • 05 - 4. Build a Simple Neural Network Using Keras
  • 01 - The Keras Sequential model
  • 02 - Use case and determine evaluation metric
  • 03 - Data checks and data preparation
  • 04 - Data preprocessing
  • 05 - Train the neural network using Keras
  • 06 - Challenge Build a neural network
  • 07 - Solution Build a neural network

  • 06 - 5. Best Practices for Optimizing a Neural Network
  • 01 - Overfitting and underfitting Two common ANN problems
  • 02 - Hyperparameters and neural networks
  • 03 - How do you improve model performance
  • 04 - Regularization techniques to improve overfitting models
  • 05 - Challenge Manually tune hyperparameters
  • 06 - Solution Manually tune hyperparameters

  • 07 - Conclusion
  • 01 - Next steps
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    شناسه: 24070
    حجم: 183 مگابایت
    مدت زمان: 106 دقیقه
    تاریخ انتشار: 12 آذر 1402
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