This book takes a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code. Graphics and examples make these computer science concepts understandable and relevant. You can use these techniques with any language; examples in the book are in JavaScript, Python, and Ruby.
Use Big O notation, the primary tool for evaluating algorithms, to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software.
Jay Wengrow brings to this book the key teaching practices he developed as a web development bootcamp founder and educator. Use these techniques today to make your code faster and more scalable. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.
The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.
Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you.
Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. In this one-of-a-kind text, author V. Anton Spraul breaks down the ways that programmers solve problems and teaches you what other introductory books often ignore: how to Think Like a Programmer.
Each chapter tackles a single programming concept, like classes, pointers, and recursion, and open-ended exercises throughout challenge you to apply your knowledge. As the most skillful programmers know, writing great code is a creative art—and the first step in creating your masterpiece is learning to Think Like a Programmer. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby.
This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. About the Book Grokking Algorithms is a friendly take on this core computer science topic. Download ». Grokking Algorithms by Aditya Y. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.
By the end of this video editions course, you will have mastered widely applicable algorithms as well as how and when to use them. Embracing this new world means mastering the core algorithms at the heart of AI. About the book Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts. In the next chapter, we'll build on the skills we've learned here by looking at how the phase estimation algorithm enables spin-off technologies like quantum sensors.
About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. Well, do that! Go to the book's Notebooks and copy Serrano G. Luis, Luis Serrano.
Thus you can see that there is a gray area between parametric and nonparametric algorithms. Even parametric algorithms are somewhat influenced by the number of classes in the data, even if they aren't explicitly counting patterns. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems.
This book combines annotated Python code with intuitive explanations to explore DRL techniques. Grokking Simplicity lays out how to use functional programming in a professional environment to write a codebase that''s easier to test and reuse, has fewer bugs, and is better at handling the asynchronous nature of distributed systems.
This book is intended as a manual on algorithm design, providing access to combinatorial algorithm technology for both students and computer professionals.
The most comprehensive guide to designing practical and efficient algorithms! About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! Grokking Functional Programming is a practical book written especially for object-oriented programmers. Grokking Functional Programming teaches you first to break down problems in a new way so you can approach them from a FP mindset.
For this edition, Lafore has rewritten each program to improve its operation, clarify the algorithms it illustrates, and reflect the latest versions of the Java 2 SDK.
Essential Information about Algorithms and Data Structures A Classic Reference The latest version of Sedgewick, s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades.
Author : Aditya Bhargava Publisher: Simon and Schuster ISBN: Category: Computers Page: View: Download » Summary 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.
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 Manning Publications' YouTube channel.
Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning. 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. 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.
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 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.
He blogs on programming at adit. The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies. Which of these hash functions are consistent?
Using hash tables for lookups Your phone has a handy phonebook built in. Each name has a phone number associated with it.
Hash tables make it easy to model a relationship from one item to another. Hash tables are used for lookups on a much larger scale. Your computer has to translate adit. Use cases 81 For any website you go to, the address has to be translated to an IP address.
Wow, mapping a web address to an IP address? Sounds like a perfect use case for hash tables! Hash tables are one way to provide this functionality. Naturally, every person can vote just once. When someone comes in to vote, you ask for their full name. If their name is on the list, this person has already voted—kick them out! Otherwise, you add their name to the list and let them vote.
Now suppose a lot of people have come in to vote, and the list of people who have voted is really long. Otherwise, it returns None. You can use this to check if someone has already voted! Checking for duplicates is very fast with a hash table. Using hash tables as a cache One inal use case: caching. If you work on a website, you may have heard of caching before as a good thing to do.
Suppose you visit facebook. You get a web page. It takes a couple of seconds to collect all that activity and shows it to you. Is there a way to make Facebook faster and have its servers do less work at the same time?
Suppose you have a niece who keeps asking you about planets. Each time you go to facebook. Everyone sees the same login page.
Instead, it memorizes what the home page looks like and sends it to you. You can answer instantly. Caching is a common way to make things faster. All big websites use caching. And that data is cached in a hash! So it needs a mapping from page URL to page data. When you visit a page on Facebook, it irst checks whether the page is stored in the hash. Before you return the data, though, you save it in the cache.
But you still care about performance! To understand the performance of hash tables, you irst need to understand what collisions are. I told you that a hash function always maps diferent keys to diferent slots in the array. Suppose your array contains 26 slots. And your hash function is really simple: it assigns a spot in the array alphabetically. Collisions 87 Maybe you can already see the problem. You want to put the price of apples in your hash. You get assigned the irst slot. You get assigned the second slot.
Everything is going so well! But now you want to put the price of avocados in your hash. You get assigned the irst slot again. Oh no! Apples have that slot already! What to do? Collisions are bad, and you need to work around them.
So you start a linked list at that slot. If the linked list is small, no big deal—you have to search through three or four elements. But suppose you work at a grocery store where you only sell produce that starts with the letter A.
Hey, wait a minute! And that slot has a giant linked list! Every single element in this hash table is in the linked list. Your hash function mapped all the keys to a single slot. Ideally, your hash function would map keys evenly all over the hash. Hash functions are important. A good hash function will give you very few collisions. So how do you pick a good hash function? Performance You started this chapter at the grocery store. You wanted to build something that would give you the prices for produce instantly.
Well, hash tables are really fast. In the average case, hash tables take O 1 for everything. O 1 is called constant time. It means the time taken will stay the same, regardless of how big the hash table is. For example, you know that simple search takes linear time. Binary search is faster—it takes log time: Looking something up in a hash table takes constant time. Getting an item out of an array takes constant time. In the average case, hash tables are really fast.
Look at the average case for hash tables. Hash tables are as fast as arrays at searching getting a value at an index. But in the worst case, hash tables are slow at all of those. And to do that, you need to avoid collisions. Whatever programming language you use will have an implementation of hash tables built in. You can use the built-in hash table and assume it will have good performance.
The next section gives you a peek under the hood. Load factor he load factor of a hash table is easy to calculate. Hash tables use an array for storage, so you count the number of occupied slots in an array. Load factor measures how many empty slots remain in your hash table.
Suppose you need to store the price of produce items in your hash table, and your hash table has slots. In the best case, each item will get its own slot.
What if your hash table has only 50 slots? Having a load factor greater than 1 means you have more items than slots in your array. Once the load factor starts to grow, you need to add more slots to your hash table. For example, suppose you have this hash table that is getting pretty full. You need to resize this hash table. Much better! A good rule of thumb is, resize when your load factor is greater than 0. But averaged out, hash tables take O 1 even with resizing. A good hash function A good hash function distributes values in the array evenly.
A bad hash function groups values together and produces a lot of collisions. What is a good hash function? You could use that as your hash function. Suppose you have these four hash functions that work with strings: a. Use the length of the string as the index. Use the irst character of the string as the index. So, all strings starting with a are hashed together, and so on. For a string, the hash function is the sum of all the characters modulo the size of the hash.
For each of these examples, which hash functions would provide a good distribution? Assume a hash table size of 10 slots. You need a hash function that minimizes collisions. Breadth-irst search allows you to ind the shortest distance between two things.
But shortest distance can mean a lot of things! You want to get there by bus, with the minimum number of transfers. Here are your options. Well, can you get there in one step? Here are all the places you can get to in one step. Can you get there in two steps? What about three steps? Now the Golden Gate Bridge shows up. So it takes three steps to get from Twin Peaks to the bridge using this route.
It could be the smallest number of moves to checkmate in a game of chess. Model the problem as a graph. Solve the problem using breadth-irst search.
What is a graph? A graph models a set of connections. For example, suppose you and your friends are playing poker, and you want to model who owes whom money.
Graph of people who owe other people poker money Alex owes Rama money, Tom owes Adit money, and so on. Each graph is made up of nodes and edges. Graphs are made up of nodes and edges. A node can be directly connected to many other nodes. Graphs are a way to model how diferent things are connected to one another. Breadth-irst search We looked at a search algorithm in chapter 1: binary search. Breadth- irst search is a diferent kind of search algorithm: one that runs on graphs.
Are you connected to a mango seller on Facebook? Well, you can search through your friends. First, make a list of friends to search. Breadth-irst search Now, go to each person in the list and check whether that person sells mangoes. Suppose none of your friends are mango sellers. Each time you search for someone from the list, add all of their friends to the list.
Remember, the goal is to ind one mango seller in your network. Is there a mango seller in your network? Who is the closest mango seller? Can you ind the closest mango seller? For example, your friends are irst-degree connections, and their friends are second-degree connections. Well, breadth-irst search already does this!
Pop quiz: who will be checked irst, Claire or Anuj? Answer: Claire is a irst- degree connection, and Anuj is a second-degree connection. So Claire will be checked before Anuj. Another way to see this is, irst-degree connections are added to the search list before second-degree connections. You just go down the list and check people to see whether each one is a mango seller.
Breadth-irst search not only inds a path from A to B, it also inds the shortest path. Well, Anuj is a second-degree contact, and Claire is a irst-degree contact. Queues A queue works exactly like it does in real life.
Suppose you and your friend are queueing up at the bus stop. A queue works the same way. Queues are similar to stacks. Instead, there are two only operations, enqueue and dequeue. You can use this for your search list! People who are added to the list irst will be dequeued and searched irst.
Implementing the graph Implementing the graph First, you need to implement the graph in code. A graph consists of several nodes. And each node is connected to neighboring nodes. Luckily, you know a data structure that lets you express relationships: a hash table!
Remember, a hash table allows you to map a key to a value. In this case, you want to map a node to all of its neighbors. A graph is just a bunch of nodes and edges, so this is all you need to have a graph in Python. What about a bigger graph, like this one? For example, both of these graphs are equal.
Note When updating queues, I use the terms enqueue and dequeue. Push is almost always the same thing as enqueue, and pop is almost always the same thing as dequeue. Make a queue to start. Implementing the algorithm And so on. Alice and Bob share a friend: Peggy. So once you search a person, you should mark that person as searched and not search them again. Suppose the mango seller graph looked like this.
To start, the search queue contains all of your neighbors. Now you check Peggy. And so on. So the running time is at least O number of edges. You also keep a queue of every person to search.
Adding one person to the queue takes constant time: O 1. Doing this for every person will take O number of people total. From this graph, you can make a list of the order in which I need to do my morning routine: 1. Wake up. Brush teeth. Eat breakfast. Make a valid list for this graph. You could say that this list is sorted, in a way. If task A depends on task B, task A shows up later in the list.
You could topologically sort the graph and get a list of tasks to do, in order. Implementing the algorithm Suppose you have a family tree.
A tree is a special type of graph, where no edges ever point back. Otherwise, you might end up in an ininite loop. But suppose you add travel times to those segments.
You used breadth-irst search in the last chapter. Breadth-irst search will ind you the path with the fewest segments the irst graph shown here. What if you want the fastest path instead the second graph? Each segment has a travel time in minutes. But that path takes 7 minutes. Update the costs of the neighbors of this node. Calculate the inal path. Step 1: Find the cheapest node. How long does it take to get to each node?
It takes 6 minutes to get to node A and 2 minutes to get to node B. Hey, you just found a shorter path to node A! It used to take 6 minutes to get to node A. When you ind a shorter path for a neighbor of B, update its cost. Step 1 again: Find the node that takes the least amount of time to get to.
Woo, it takes 6 minutes to get to the inish now! Find the cheapest node. If so, update their costs. Coming up in the next section!
But irst let me clarify some terminology. A graph with weights is called a weighted graph. A graph without weights is called an unweighted graph. Terminology To calculate the shortest path in an unweighted graph, use breadth-irst search. Graphs can also have cycles. A cycle looks like this. It means you can start at a node, travel around, and end up at the same node. Would it make sense to follow the cycle?
Well, you can use the path that avoids the cycle. Or you can follow the cycle. You could even follow the cycle twice if you wanted. So following the cycle will never give you the shortest path. Finally, remember our conversation about directed versus undirected graphs from chapter 6?
An undirected graph means that both nodes point to each other. With an undirected graph, each edge adds another cycle. Rama is trying to trade a music book for a piano. With a little bit of money, Rama can trade his way from a piano book to a real piano. Now he just needs to igure out how to spend the least amount of money to make those trades. In this graph, the nodes are all the items Rama can trade for. How is Rama going to igure out the path from the book to the piano where he spends the least dough?
Before you start, you need some setup. Make a table of the cost for each node. To calculate the inal path, you also need a parent column on this table. Is there a cheaper way to trade for the poster?
Answer: No. If you take the path toward the school, that takes 2 minutes. If you take the path toward the park, that takes 6 minutes. Is there any way you can take the path toward the park, and end up at the school, in less than 2 minutes? On the other hand, can you ind a faster path to the park? Back to the music example. Step 2: Figure out how long it takes to get to its neighbors the cost. You have prices for the bass guitar and the drum set in the table.
Step 2 again: Update the values of all of its neighbors. Hey, you updated the price of both the drums and the guitar! So you set the LP as the new parent for both instruments. Update its neighbors. Ok, you inally have a price for the piano, by trading the guitar for the piano.
So you set the guitar as the parent. Finally, the last node, the drum set. Rama can get the piano even cheaper by trading the drum set for the piano instead. Now, as I promised, you need to igure out the path. To start with, look at the parent for piano. So you follow this edge.
Piano has drums as its parent. And drums has the LP as its parent. So Rama will trade the LP for the drums. By following the parents backward, you now have the complete path.
It can be about minimizing something. In this case, Rama wanted to minimize the amount of money he spent. Negative-weight edges In the trading example, Alex ofered to trade the book for two items.
How would you show this on the graph? Now Rama has two ways to get to the poster. Now, if you remember, Rama can trade the poster for the drums. Negative-weight edges break the algorithm.
First, make the table of costs. Next, ind the lowest-cost node, and update the costs for its neighbors. In this case, the poster is the lowest-cost node. Update the costs for its neighbors. But you just found a cheaper way to the poster! Here are the inal costs. Bellman-Ford is out of the scope of this book, but you can ind some great explanations online. First, you need to implement the graph. For example, Start has two neighbors, A and B.
Why not just use another hash table? What if you want to ind the weights of those edges? Implementation he full graph hash table looks like this.
Next you need a hash table to store the costs for each node. You know it takes 2 minutes from Start to node B. You know it takes 6 minutes to get to node A although you may ind a path that takes less time. Can you represent ininity in Python? Implementation Find the node with the lowest cost. Get the cost and neighbors of that node. Loop through the neighbors. Each node has a cost. Update the cost. How long does it take to get to the inish if you go through node B?
It takes 7 minutes. Implementation Set the new cost and the new parent for the Finish node. Ok, you updated the costs for all the neighbors of node B. Mark it as processed.
Find the next node to process. Get the cost and neighbors for node A. Currently it takes 7 minutes to get to the Finish node. How long would it take to get there if you went through node A?
I hope the walkthrough helped you understand the algorithm a little better. You get a list of classes. You want to hold as many classes as possible in this classroom. How do you pick what set of classes to hold, so that you get the biggest set of classes possible? Sounds like a hard problem, right? Actually, the algorithm is so easy, it might surprise you. Pick the class that ends the soonest. Now, you have to pick a class that starts ater the irst class. Again, pick the class that ends the soonest.
Art ends the soonest, at a. Now you need the next class that starts ater a. English is out because it conlicts with Art, but Math works.
Finally, CS conlicts with Math, but Music works. A greedy algorithm is simple: at each step, pick the optimal move. In this case, each time you pick a class, you pick the class that ends the soonest. Believe it or not, this simple algorithm inds the optimal solution to this scheduling problem!
But you can only take what you can it in your knapsack. What algorithm do you use? Again, the greedy strategy is pretty simple: 1. Pick the most expensive thing that will it in your knapsack.
Pick the next most expensive thing that will it in your knapsack. For example, suppose there are three items you can steal. The knapsack problem Your knapsack can hold 35 pounds of items. But wait! But it gets you pretty close. Sometimes all you need is an algorithm that solves the problem pretty well. You need to pack your truck with boxes.
How would you pick boxes to maximize space? Come up with a greedy strategy. Will that give you the optimal solution? How can you maximize the point total seeing all the things you really want to see during your stay?
You want to reach listeners in all 50 states. You have to decide what stations to play on to reach all those listeners. You have a list of stations. How do you igure out the smallest set of stations you can play on to cover all 50 states?
List every possible subset of stations. The set-covering problem 2. From these, pick the set with the smallest number of stations that covers all 50 states. But with all the examples here, think about what will happen if you have a lot of items. It takes much longer if you have more stations. Suppose you can calculate 10 subsets per second. What can you do? Approximation algorithms Greedy algorithms to the rescue!
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