Overview
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Dynamic programming makes it possible to solve challenging problems efficiently. Learn what it is, how and when to apply it to your algorithms, and how it's used in applications.
Syllabus
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Introduction
- The importance of dynamic programming
- What you should know
- What is dynamic programming?
- The Fibonacci sequence
- Speeding up calculations with memoization
- Bottom-up approach to dynamic programming
- Recap of dynamic programming
- Planting flowers in a flowerbox
- Breaking down the flowerbox problem into subproblems
- Solving the flowerbox problem in Python
- How many ways can you make change?
- Breaking down the change-making problem into subproblems
- Solving the change-making problem in Python
- What is content-aware image resizing?
- Preprocessing: Defining the energy of an image
- Project: Calculating the energy of an image
- Solution: Calculating the energy of an image
- Using dynamic programming to find low-energy seams
- Project: Finding low-energy seams
- Solution: Finding low-energy seams
- Project: Using backpointers to reconstruct seams
- Solution: Using backpointers to reconstruct seams
- Project: Removing low-energy seams
- Solution: Removing low-energy seams
- What is a Hidden Markov Model?
- Modeling a Hidden Markov Model in Python
- Inferring the most probable state sequence
- Breaking down state inference into subproblems: The Viterbi algorithm
- Implementing the Viterbi algorithm in Python
- More applications of Hidden Markov Models
- Training Hidden Markov Models
- Next steps
