Software Engineering for Machine Learning
  • Preface
  • Introduction to Python
  • Version Control
  • Code Review
  • Testing
  • Web Services
  • Continuous Integration
  • Graphics
  • Reproducibility
  • Machine Learning
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis
  • Practical Considerations of Clustering
  • Floating Point and Finite Precision
  • Supervised Machine Learning
  • Gradient Descent
  • Support Vector Machines
  • Neural Networks
  • Building the Book using Sphinx, GitHub Pages, and Travis
  • Sphinx Demonstration
Software Engineering for Machine Learning
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© Copyright 2019, Yung-Hsiang Lu and George K. Thiruvathukal

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