This book does the impossible: it makes math fun and easy!
Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.
Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on YouTube
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1. Introduction To Algorithms
1.1. Introduction
1.1.1. What you’ll learn about performance
1.1.2. What you’ll learn about solving problems
1.2. Binary Search
1.2.1. A better way to search
1.2.2. Running time
1.3. Big-O-notation
1.3.1. Algorithm running times grow at different rates
1.3.2. Visualizing dfferent Big O run times
1.3.3. Big O establishes a worst-case runtime
1.3.4. Some common Big O run times
1.3.5. The traveling salesperson
1.4. Recap
2. Selection Sort
2.1. How Memory Works
2.2. Arrays And Linked Lists
2.2.1. Linked Lists
2.2.2. What arrays are good for
2.2.3. Terminology
2.2.4. More insertions and deletions
2.2.5. Deletions
2.3. Selection Sort
2.4. Recap
3. Recursion
3.1. Recursion
3.2. Base Case And Recursive Case
3.3. The Stack
3.3.1. The call stack
3.3.2. The call stack with recursion
3.4. Recap
4. Quicksort
4.1. Divide And Conquer
4.2. Quicksort
4.3. Big O Notation Revisited
4.3.1. Merge sort vs. quicksort
4.3.2. Average case vs. worst case
4.4. Recap
5. Hash Tables
5.1. Hash Functions
5.2. Use Cases
5.2.1. Using hash tables for lookups
5.2.2. Preventing duplicate entries
5.2.3. Using hash tables as a cache
5.2.4. Recap
5.3. Collisions
5.4. Performance
5.4.1. Load factor
5.4.2. A good hash function
5.5. Recap
6. Breadth-first Search
6.1. Introduction To Graphs
6.2. What is a graph?
6.3. Breadth-first Search
6.3.1. Finding the shortest path
6.3.2. Queues
6.4. Implementing The Graph
6.5. Implementing The Algorithm
6.5.1. Running time
6.6. Recap
7. Dijkstra’s Algorithm
7.1. Working with Dijkstra’s Algorithm
7.2. Terminology
7.3. Trading For A Piano
7.4. Negative Weight Edges
7.5. Implementation
7.6. Recap
8. Greedy Algorithms
8.1. The Classroom Scheduling Problem
8.2. The Knapsack Problem
8.3. The Set-Covering Problem
8.3.1. Approximation Algorithms
8.4. NP Complete Problems
8.4.1. How do you tell if a problem is NP-Complete?
8.5. Recap
9. Dynamic Programming
9.1. The Knapsack Problem
9.1.1. The simple solution
9.1.2. Dynamic programming
9.2. Knapsack Problem Faq
9.2.1. What happens if we add an item?
9.2.2. What happens if we change the order of the rows?
9.2.3. Can you fill in the grid column-wise instead of row-wise?
9.2.4. What happens if we add a smaller item?
9.2.5. Can you steal fractions of an item?
9.2.6. Optimizing your travel itinerary
9.2.7. Handling items that depend on each other
9.2.8. Is it possible that the solution will require more than 2 sub-knapsacks?
9.2.9. Is it possible that the best solution doesn't fill the knapsack completely?
9.3. Longest Common Substring
9.3.1. Making the grid
9.3.2. Filling in the grid
9.3.3. The solution
9.3.4. Longest common subsequence
9.3.5. Longest common subsequence — solution
9.4. Recap
10. K Nearest Neighbors
10.1. Classifying Oranges Vs Grapefruit
10.2. Building A Recommendations System
10.2.1. Feature Extraction
10.2.2. Regression
10.2.3. Picking good features
10.3. Introduction To Machine Learning
10.3.1. OCR
10.3.2. Building a spam filter
10.3.3. Predicting the stock market
10.4. Recap
11. Where To Go Next
11.1. Trees
11.2. Inverted Indexes
11.3. The Fourier Transform
11.4. Parallel Algorithms
11.5. Map Reduce
11.5.1. Why are distributed algorithms useful?
11.5.2. The "map" function
11.5.3. The "reduce" function
11.6. Bloom Filters And Hyperloglog
11.6.1. Bloom Filters
11.6.2. Hyperloglog
11.7. The Sha Algorithms
11.7.1. Comparing files
11.7.2. Checking passwords
11.8. Locality Sensitive Hashing
11.9. Diffie-hellman Key Exchange
11.10. Linear Programming
11.11. Epilogue
About the Technology
An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you?ll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs.
About the book
Grokking Algorithms is a friendly take on this core computer science topic. In it, you?ll learn how to apply common algorithms to the practical programming problems you face every day. You?ll start with tasks like sorting and searching. As you build up your skills, you?ll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them.
What's inside
- Covers search, sort, and graph algorithms
- Over 400 pictures with detailed walkthroughs
- Performance trade-offs between algorithms
- Python-based code samples
About the reader
This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms.
About the author
Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io.
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Do you want to treat yourself to learning algorithms in the same way that you would read your favorite novel? If so, this is the book you need!
In today’s world, there is no aspect of our lives that isn’t optimized by some algorithm. Let this be the first book you pick up if you want a well-explained introduction to the topic.
Algorithms are not boring! This book was fun and insightful for both my students and me.