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A fun read to sharpen your classic programming skills and to bring your Swift programming to another level.
Classic Computer Science Problems in Swift deepens your Swift language skills by exploring foundational coding techniques 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. All examples are written in Swift 4.1.
About the Technology
Don't just learn another language. Become a better programmer instead. Today's awesome iOS apps stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills in Swift, and you'll be ready for AI, data-centric programming, machine learning, and the other development challenges that will define the next decade.
About the book
Classic Computer Science Problems in Swift deepens your Swift language skills by exploring foundational coding techniques 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. You'll appreciate author David Kopec's amazing ability to connect the core disciplines of computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!
Table of Contents takes you straight to the bookdetailed table of contents
Introduction
0.1 Why Swift?
0.2 What is a classic computer science problem?
0.3 What kinds of problems are in this book?
0.4 Who is this book for?
0.5 Swift Versioning and Tools
0.6 No Graphics, No UI Code
1 Small Problems
1.1 The Fibonacci Sequence
1.1.1 A First Recursive Attempt
1.1.2 Utilizing Base Cases
1.1.3 Memoization to the Rescue
1.1.4 Keep it Simple, Fibonacci
1.2 Trivial Compression
1.3 Unbreakable Encryption
1.3.1 Getting Our Data in Order
1.3.2 Encrypting and Decrypting
1.4 Calculating Pi
1.5 The Towers of Hanoi
1.5.1 Modeling the Towers
1.5.2 Solving The Towers of Hanoi
1.6 Real World Applications
1.7 Exercises
2 Search Problems
2.1 DNA Search
2.1.1 Storing DNA
2.1.2 Linear Search
2.1.3 Binary Search
2.1.4 A Generic Example
2.2 Maze Solving
2.2.1 Generating a Random Maze
2.2.2 Miscellaneous Maze Minutiae
2.2.3 Depth-First Search
2.2.4 Breadth-First Search
2.2.5 A* Search
2.3 Missionaries and Cannibals
2.3.1 Representing the Problem
2.3.2 Solving
2.4 Real World Applications
2.5 Exercises
3 Constraint Satisfaction Problems
3.1 Building a Constraint Satisfaction Problem Framework
3.2 The Australian Map Coloring Problem
3.3 The Eight Queens Problem
3.4 Word Search
3.5 SEND+MORE=MONEY
3.6 Circuit Board Layout
3.7 Real World Applications
3.8 Exercises
4 Graph Problems
4.1 Building a Graph Framework
4.1.1 A Concrete Implementation of Edge
4.1.2 A Concrete Implementation of Graph
4.2 Finding the Shortest Path
4.2.1 Defining a Path
4.2.2 Revisiting Breadth-First Search (BFS)
4.3 Minimizing the Cost of Building the Network
4.3.1 Workings with Weights
4.3.2 Finding the Minimum Spanning Tree
4.4 Finding Shortest Paths in a Weighted Graph
4.4.1 Dijkstra’s Algorithm
4.5 Real World Applications
4.6 Exercises
5 Genetic Algorithms
5.1 Preliminaries
5.2 A Generic Genetic Algorithm
5.3 A Naive Test
5.4 SEND+MORE=MONEY Revisited
5.5 Challenges for Genetic Algorithms
5.6 Real World Applications
5.7 Exercises
6 K-Means Clustering
6.1 Preliminaries
6.2 The K-Means Clustering Algorithm
6.3 Clustering Governors by Age and Longitude
6.4 K-Means Clustering Problems and Extensions
6.5 Real World Applications
6.6 Exercises
7 Fairly Simple Neural Networks
7.1 Biological Basis?
7.2 Artificial Neural Networks
7.2.1 Neurons
7.2.2 Layers
7.2.3 Backpropagation
7.2.4 The Big Picture
7.3 Preliminaries
7.3.1 Help with Randomization
7.3.2 Fast Arithmetic
7.3.3 The Activation Function
7.4 Building the Network
7.4.1 Neurons
7.4.2 Layers
7.4.3 The Network
7.5 Classification Problems
7.5.1 Normalizing Data
7.5.2 The Classic Iris Data Set
7.5.3 Classifying Wine
7.6 Neural Network Problems and Extensions
7.7 Real World Applications
7.8 Exercises
8 Miscellaneous Problems
8.1 The Knapsack Problem
8.2 The Travelling Salesman Problem
8.2.1 The Naive Approach
8.2.2 Taking it to the Next Level
8.3 Phone Number Mnemonics
8.4 Tic-Tac-Toe
8.4.1 Managing State
8.4.2 Minimax
8.5 Real World Applications
8.6 Exercises
Appendixes
Appendix A: A Glossary
Appendix B: B More Resources
B.1 Swift
B.2 iOS Development
B.3 Mac Development
B.4 Algorithms and Data Structures
B.5 Artificial Intelligence
B.6 Functional Programming
B.7 Open Source Projects Mentioned in this Book
Appendix C: C A Brief History of Swift
C.1 A Brief History of Programming Language Paradigms Incorporated in Swift
C.2 Programming Languages at Apple before Swift
C.3 Swift History
C.3.1 Milestones
C.3.2 Swift on Other Platforms
C.4 Swift Future Directions
What's inside
- Breadth-first, depth-first, and A* search algorithms
- Constraint-satisfaction problems
- Solving problems with graph algorithms
- Neural networks, genetic algorithms, and more
- All examples written in Swift 4.1
About the author
David Kopec is an assistant professor of computer science and innovation at Champlain College in Burlington, Vermont. He is an experienced iOS developer and the author of Dart for Absolute Beginners.
We interviewed David as a part of our Six Questions series. Check it out here.
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