English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 38 Lessons (4h 55m) | 709 MB
Classic Computer Science Problems in Python deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with time-tested scenarios and algorithms. As you work through examples in search, clustering, graphs, and more, you’ll remember important things you’ve forgotten and discover classic solutions to your “new” problems!
Computer science problems that seem new or unique are often rooted in classic algorithms, coding techniques, and engineering principles. And classic approaches are still the best way to solve them! Understanding these techniques in Python expands your potential for success in web development, data munging, machine learning, and more.
Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You’ll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You’ll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!
Inside:
- Search algorithms
- Common techniques for graphs
- Neural networks
- Genetic algorithms
- Adversarial search
- Uses type hints throughout
- Covers Python 3.7
Table of Contents
Chapter 1. Small problems
1 Small problems
2 Trivial compression
3 Unbreakable encryption
4 The Towers of Hanoi
Chapter 2. Search problems
5 Search problems
6 Binary search
7 Maze solving
8 Breadth-first search
9 A search
10 Missionaries and cannibals
Chapter 3. Constraint-satisfaction problems
11 Constraint-satisfaction problems
12 The Australian map-coloring problem
13 SEND+MORE=MONEY
Chapter 4. Graph problems
14 Graph problems
15 Building a graph framework
16 Finding the shortest path
17 Minimizing the cost of building the network
18 Finding shortest paths in a weighted graph
Chapter 5. Genetic algorithms
19 Genetic algorithms
20 A generic genetic algorithm
21 A naive test
22 Optimizing list compression
Chapter 6. K-means clustering
23 K-means clustering
24 The k-means clustering algorithm
25 Clustering Michael Jackson albums by length
Chapter 7. Fairly simple neural networks
26 Fairly simple neural networks
27 Artificial neural networks
28 Preliminaries
29 Classification problems
30 The classic iris data set
31 Speeding up neural networks
Chapter 8. Adversarial search
32 Adversarial search
33 Tic-tac-toe
34 Connect Four
35 Minimax improvements beyond alpha-beta pruning
Chapter 9. Miscellaneous problems
36 Miscellaneous problems
37 The Traveling Salesman Problem
38 Phone number mnemonics
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