Chapter Introduction
Why Python?
What is a classic computer science problem?
What kinds of problems are in this book?
Who is this book for?
Python versioning, source code repository, and type hints
No graphics, no UI code, just the standard library
Part of a series
Chapter 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. Automatic memoization
1.1.5. Keep it simple, Fibonacci
1.1.6. Generating Fibonacci numbers with a generator
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
Chapter 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
Euclidean distance
2.3. Missionaries and cannibals
2.3.1. Representing the problem
2.3.2. Solving
2.4. Real-world applications
2.5. Exercises
Chapter 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
Chapter 4. Graph problems
4.1. A map as a graph
4.2. Building a graph framework
4.2.1. Working with Edge and Graph
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. Workings 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
A. Glossary
B. More resources
B.1 Python
B.2 Algorithms and data structures
B.3 Artificial intelligence
B.4 Functional programming
B.5 Open source projects useful for machine learning
C. A brief introduction to type hints
C.1 What are type hints?
C.2 What do type hints look like?
C.3 Why are type hints useful?
C.4 What are the downsides of type hints?
C.5 Getting more information