The Art Of Randomness Randomized Algorithms In The Real World
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The Art of Randomness
Harness the power of randomness (and Python code) to solve real-world problems in fun, hands-on experiments—from simulating evolution to encrypting messages to making machine-learning algorithms! The Art of Randomness is a hands-on guide to mastering the many ways you can use randomized algorithms to solve real programming and scientific problems. You’ll learn how to use randomness to run simulations, hide information, design experiments, and even create art and music. All you need is some Python, basic high school math, and a roll of the dice. Author Ronald T. Kneusel focuses on helping you build your intuition so that you’ll know when and how to use random processes to get things done. You’ll develop a randomness engine (a Python class that supplies random values from your chosen source), then explore how to leverage randomness to: Simulate Darwinian evolution and optimize with swarm-based search algorithms Design scientific experiments to produce more meaningful results by making them truly random Implement machine learning algorithms like neural networks and random forests Use Markov Chain Monte Carlo methods to sample from complex distributions Hide information in audio files and images, generate art, and create music Reconstruct original signals and images from only randomly sampled data Scientific anecdotes and code examples throughout illustrate how randomness plays into areas like optimization, machine learning, and audio signals. End-of-chapter exercises encourage further exploration. Whether you’re a programmer, scientist, engineer, mathematician, or artist, you’ll find The Art of Randomness to be your ticket to discovering the hidden power of applied randomness and the ways it can transform your approach to solving problems, from the technical to the artistic.
Graph Algorithms the Fun Way
Author: Jeremy Kubica
language: en
Publisher: NO STARCH PRESS, INC
Release Date: 2024-11-19
Enter the wonderful world of graph algorithms, where you’ll learn when and how to apply these highly useful data structures to solve a wide range of fascinating (and fantastical) computational problems. Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you’ll build a solid foundation for addressing graph problems in your own projects. Explore a rich landscape of cleverly constructed scenarios where: Hedge mazes illuminate depth-first search Urban explorations demonstrate breadth-first search Intricate labyrinths reveal bridges and articulation points Strategic planning illustrates bipartite matching From fundamental graph structures to advanced topics, you will: Implement powerful algorithms, including Dijkstra’s, A*, and Floyd-Warshall Tackle puzzles and optimize pathfinding with newfound confidence Uncover real-world applications in social networks and transportation systems Develop robust intuition for when and why to apply specific graph techniques Delve into topological sorting, minimum spanning trees, strongly connected components, and random walks. Confront challenges like graph coloring and the traveling salesperson problem. Prepare to view the world through the lens of graphs—where connections reveal insights and algorithms unlock new possibilities.
Math for Programming
A one-stop-shop for all the math you should have learned for your programming career. Every great programming challenge has mathematical principles at its heart. Whether you’re optimizing search algorithms, building physics engines for games, or training neural networks, success depends on your grasp of core mathematical concepts. In Math for Programming, you’ll master the essential mathematics that will take you from basic coding to serious software development. You’ll discover how vectors and matrices give you the power to handle complex data, how calculus drives optimization and machine learning, and how graph theory leads to advanced search algorithms. Through clear explanations and practical examples, you’ll learn to: Harness linear algebra to manipulate data with unprecedented efficiency Apply calculus concepts to optimize algorithms and drive simulations Use probability and statistics to model uncertainty and analyze data Master the discrete mathematics that powers modern data structures Solve dynamic problems through differential equations Whether you’re seeking to fill gaps in your mathematical foundation or looking to refresh your understanding of core concepts, Math for Programming will turn complex math into a practical tool you’ll use every day.