2. Data Science

Python distributed programming primarily serves fields such as data science and artificial intelligence. This chapter mainly introduces background knowledge related to data science. If you have a good understanding of the relevant fundamentals, you can also skip this chapter directly.

• 2.1. Data Science Lifecycle
  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Validation
  • Deployment
  • Summary
• 2.2. Machine Learning
  • Definition of Machine Learning
  • Linear Regression
    • Simple Linear Regression
    • General Form of Linear Regression
  • Logistic Regression
• 2.3. Deep Learning
  • Deep Neural Networks
  • Forward Propagation
  • Backpropagation
  • Hyperparameters
  • Implementation Details
    • Forward Pass
    • Backward Propagation
    • Model Weight Update
    • Neural Network Training Process
  • Inference
• 2.4. Hyperparameter Optimization
  • Search Algorithms
    • Bayesian Optimization
  • Scheduler
    • Successive Halving Algorithm
    • Hyperband
    • BOHB
  • Population Based Training
• 2.5. Ecosystem and Content
  • Python Ecosystem
  • Content of This Book
  • Tutorials and Datasets in This Book