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Artificial Intelligence Foundations: Neural Networks

Offered By: LinkedIn Learning

Tags

Neural Networks Courses Machine Learning Courses Deep Learning Courses Keras Courses Hyperparameter Tuning Courses Multilayer Perceptron Courses Perceptron Courses Transformer Architecture Courses

Course Description

Overview

Learn the fundamental techniques and principles behind artificial neural networks.

Syllabus

Introduction
  • Neural networks 101: Your path to AI brilliance
  • What you should know
  • How to use the challenge exercise files
1. What Are Neural Networks?
  • Machine learning and neural networks
  • Biological neural networks
  • Artificial neural networks
  • Single-layer perceptron
2. Key Components in Neural Network Architecture
  • Multilayer perceptron
  • Layers: Input, hidden, and output
  • Transfer and activation functions
  • How neural networks learn
3. Other Types of Neural Networks
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Transformer architecture
4. Build a Simple Neural Network Using Keras
  • The Keras Sequential model
  • Use case and determine evaluation metric
  • Data checks and data preparation
  • Data preprocessing
  • Train the neural network using Keras
  • Challenge: Build a neural network
  • Solution: Build a neural network
5. Best Practices for Optimizing a Neural Network
  • Overfitting and underfitting: Two common ANN problems
  • Hyperparameters and neural networks
  • How do you improve model performance?
  • Regularization techniques to improve overfitting models
  • Challenge: Manually tune hyperparameters
  • Solution: Manually tune hyperparameters
Conclusion
  • Next steps

Taught by

Doug Rose

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