Classic Computer Science Problems in Java

Name: Classic Computer Science Problems in Java
Brand: Manning
Price: 37.67 EUR
Availability: OnlineOnly

David Kopec(Autor*in)

Manning (Verlag)

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Erschienen am 21. Dezember 2020

264 Seiten

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Beschreibung

Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms.

Summary
Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You'll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. 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!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Whatever software development problem you're facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project.

About the book
Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You'll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more.

What's inside

Recursion, memoization, and bit manipulation
Search, graph, and genetic algorithms
Constraint-satisfaction problems
K-means clustering, neural networks, and adversarial search

About the reader
For intermediate Java programmers.

About the author
David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont.

Table of Contents

1 Small problems
2 Search problems
3 Constraint-satisfaction problems
4 Graph problems
5 Genetic algorithms
6 K-means clustering
7 Fairly simple neural networks
8 Adversarial search
9 Miscellaneous problems
10 Interview with Brian Goetz

Weitere Details

Weitere Ausgaben

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Inhalt

Intro
Classic Computer Science Problems in Java
Copyright
contents
front matter
acknowledgments
about this book
liveBook discussion forum
about the author
about the cover illustration
Introduction
Who should read this book
How this book is organized: A roadmap
About the code
Other online resources
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
1.1.5 Generating Fibonacci numbers with a stream
1.2 Trivial compression
1.3 Unbreakable encryption
1.3.1 Getting the 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 A map as a graph
4.2 Building a graph framework
4.2.1 Working with Edge and UnweightedGraph
4.3 Finding the shortest path
4.3.1 Revisiting breadth-first search (BFS)
4.4 Minimizing the cost of building the network
4.4.1 Working with weights
4.4.2 Finding the minimum spanning tree
4.5 Finding shortest paths in a weighted graph
4.5.1 Dijkstra's algorithm
4.6 Real-world applications
4.7 Exercises
5 Genetic algorithms
5.1 Biological background
5.2 A generic genetic algorithm
5.3 A naive test
5.4 SEND+MORE=MONEY revisited
5.5 Optimizing list compression
5.6 Challenges for genetic algorithms
5.7 Real-world applications
5.8 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 Clustering Michael Jackson albums by length
6.5 K-means clustering problems and extensions
6.6 Real-world applications
6.7 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 Dot product
7.3.2 The activation function
7.4 Building the network
7.4.1 Implementing neurons
7.4.2 Implementing layers
7.4.3 Implementing 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 Speeding up neural networks
7.7 Neural network problems and extensions
7.8 Real-world applications
7.9 Exercises
8 Adversarial search
8.1 Basic board game components
8.2 Tic-tac-toe
8.2.1 Managing tic-tac-toe state
8.2.2 Minimax
8.2.3 Testing minimax with tic-tac-toe
8.2.4 Developing a tic-tac-toe AI
8.3 Connect Four
8.3.1 Connect Four game machinery
8.3.2 A Connect Four AI
8.3.3 Improving minimax with alpha-beta pruning
8.4 Minimax improvements beyond alpha-beta pruning
8.5 Real-world applications
8.6 Exercises
9 Miscellaneous problems
9.1 The knapsack problem
9.2 The Traveling Salesman Problem
9.2.1 The naive approach
9.2.2 Taking it to the next level
9.3 Phone number mnemonics
9.4 Real-world applications
9.5 Exercises
10 Interview with Brian Goetz
Appendix A. Glossary
Appendix B. More resources
Java
Data structures and algorithms
Artificial intelligence
Functional programming
index

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