Wow! eBook – Blog

Book Description

This newest edition examines fundamental data structures by following a consistent object-oriented framework that builds intuition and analysis skills of data structures and algorithms

Presents new figures, simpler language, and more practical motivations from real-world scenarios

Numerous illustrations, Web-based animations, and simplified mathematical analyses help readers quickly learn important concepts

From the Back Cover
A Clear, Visual Approach to Fundamental Data Structures and Algorithms

Goodrich and Tamassia’s DATA STRUCTURES AND ALGORITHMS IN JAVA, 5/E, incorporates the object-oriented design paradigm, using Java as the implementation language. The authors provide intuition, description, and analysis of fundamental data structures and algorithms. Numerous illustrations, web-based animations, and simplified mathematical analyses justify important analytical concepts.

In the Second Edition, the authors have improved their text by simplifying advanced topics, including many new exercises, and revising most Java code exmples. You’ll also find updated and expanded coverage of Internet-related topics, Object-oriented design, and the Java programming language, including the Collections framework and Design Patterns.

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Book Description

Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs — with just enough math to let you understand and analyze algorithm performance.

With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate.

With this book, you will:

• Solve a particular coding problem or improve on the performance of an existing solution
• Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use
• Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips
• Learn the expected performance of an algorithm, and the conditions it needs to perform at its best
• Discover the impact that similar design decisions have on different algorithms
• Learn advanced data structures to improve the efficiency of algorithms

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Book Description

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it.

Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:

• Collaborative filtering techniques that enable online retailers to recommend products or media
• Methods of clustering to detect groups of similar items in a large dataset
• Search engine features — crawlers, indexers, query engines, and the PageRank algorithm
• Optimization algorithms that search millions of possible solutions to a problem and choose the best one
• Bayesian filtering, used in spam filters for classifying documents based on word types and other features
• Using decision trees not only to make predictions, but to model the way decisions are made
• Predicting numerical values rather than classifications to build price models Download Now »

Book Description

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You’ll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you’ll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You’ll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

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Book Description

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

• Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
• Use linear regression to predict the number of page views for the top 1,000 websites
• Learn optimization techniques by attempting to break a simple letter cipher
• Compare and contrast U.S. Senators statistically, based on their voting records
• Build a “whom to follow” recommendation system from Twitter data

Table of Contents
Chapter 1. Using R
Chapter 2. Data Exploration Download Now »

Book Description

Expand your Python skills by working with data structures and algorithms in a refreshing context—through an eye-opening exploration of complexity science. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.

You’ll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

• Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables
• Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
• Get starter code and solutions to help you re-implement and extend original experiments in complexity
• Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics
• Examine case studies of complex systems submitted by students and readers

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