Artificial Intelligence Foundations: Neural Networks
1h 35mBeginner2026-01-29
Authors
Gwendolyn Stripling
Course details
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.
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.
Skills covered
JupyterKerasTensorFlowpandasNeural Networks and Deep LearningMachine LearningPythonGoogleFoundationsArtificial Intelligence (AI)Open Source
Concepts
Introduction
- Neural networks 101 - Your path to AI brilliance
- What you should know
What Are Neural Networks
- Neural networks - The building blocks of generative AI
- Machine learning and neural networks
- Neural network fundamentals
- The need for multilayer networks
Key Components in Neural Network Architecture
- Layers - Input, hidden, and output
- Transfer and activation functions
- How neural networks learn
Other Types of Neural Networks
- Convolutional neural networks (CNN)
- Transformer architecture - The model that redefined modern AI
- Why we need more than CNNs
- Self-attention in vision transformers
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
- How to use the challenge exercise files
- Challenge - Build a neural network
- Solution - Build a neural network
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