5. Neural Networks

Syllabus Points Covered

Software automation

Algorithms in machine learning

• Investigate common applications of key ML algorithms

  • image recognition

• Research models used by software engineers to design and analyse ML

  • neural networks

Programming for automation

• Apply neural network models using an OOP to make predictions

Chapter Contents

• 5.1. Deep Learning
  • 5.1.1. Glossary
• 5.2. Neural Networks
  • 5.2.1. Glossary
• 5.3. RGB to Hue and Saturation
• 5.4. Information Flow: Making a Prediction
  • 5.4.1. Glossary
• 5.5. Calculating Errors
• 5.6. Training a Neural Network
  • 5.6.1. Glossary
• 5.7. Building a Neural Network for Regression
  • 5.7.1. Visualising Learning
• 5.8. Problem and Model Analysis
  • 5.8.1. Possible Reasons For Why Our Network Underperforms
• 5.9. Neural Networks for Classification
• 5.10. Building a Neural Network For Classification
• 5.11. More Advanced Neural Networks
  • 5.11.1. Dense Neural Network
  • 5.11.2. Convolution Neural Networks
  • 5.11.3. Recurrent Neural Networks
  • 5.11.4. Autoencoders
  • 5.11.5. Generative Adversarial Networks
  • 5.11.6. Transformers