English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 25m | 193 MB
Having a clearer picture of dynamic programming (DP) can take your coding to the next level. It’s a technique that makes it possible to adeptly solve difficult problems, which is why it comes up in interviews and is used in applications like machine learning. In this course, learn about the uses of DP, how to determine when it’s an appropriate tactic, how it produces efficient and easily understood algorithms, and how it’s used in real-world applications. Compare different approaches to computing the Fibonacci Sequence and learn how to visualize the problem as a directed acyclic graph. Explore the different variations of DP that you’re likely to encounter by working through a series of increasingly complex challenges. Plus, build a content-aware image resizing application with these new concepts at its core.
Table of Contents
Introduction
1 The importance of dynamic programming
2 What you should know
Introduction to Dynamic Programming
3 What is dynamic programming
4 The Fibonacci sequence
5 Speeding up calculations with memoization
6 Bottom-up approach to dynamic programming
7 Recap of dynamic programming
Examples of Dynamic Programming
8 Planting flowers in a flowerbox
9 Breaking down the flowerbox problem into subproblems
10 Solving the flowerbox problem in Python
11 How many ways can you make change
12 Breaking down the change-making problem into subproblems
13 Solving the change-making problem in Python
Real-World Dynamic Programming Content-Aware Image Resizing
14 What is content-aware image resizing
15 Preprocessing Defining the energy of an image
16 Project Calculating the energy of an image
17 Solution Calculating the energy of an image
18 Using dynamic programming to find low-energy seams
19 Project Finding low-energy seams
20 Solution Finding low-energy seams
21 Project Using backpointers to reconstruct seams
22 Solution Using backpointers to reconstruct seams
23 Project Removing low-energy seams
24 Solution Removing low-energy seams
Dynamic Programming for Machine Learning Hidden Markov Models
25 What is a Hidden Markov Model
26 Modeling a Hidden Markov Model in Python
27 Inferring the most probable state sequence
28 Breaking down state inference into subproblems The Viterbi algorithm
29 Implementing the Viterbi algorithm in Python
30 More applications of Hidden Markov Models
31 Training Hidden Markov Models
Conclusion
32 Next steps
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