Books by Allen B. Downey

Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.

If youre a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineeringdata structures and algorithmsin a way thats clearer, more concise, and more engaging than other materials.By emphasizing practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. Youll explore the important classes in the Java collections framework (JCF), how theyre implemented, and how theyre expected to perform. Each chapter presents hands-on exercises supported by test code online.Use data structures such as lists and maps, and understand how they workBuild an application that reads Wikipedia pages, parses the contents, and navigates the resulting data treeAnalyze code to predict how fast it will run and how much memory it will requireWrite classes that implement the Map interface, using a hash table and binary search treeBuild a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query resultsOther books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.

If you understand basic mathematics and know how to program with Python, youre ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how theyre applied in the real world. In the first chapter alone, youll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds.Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material.Youll explore:Periodic signals and their spectrumsHarmonic structure of simple waveformsChirps and other sounds whose spectrum changes over timeNoise signals and natural sources of noiseThe autocorrelation function for estimating pitchThe discrete cosine transform (DCT) for compressionThe Fast Fourier Transform for spectral analysisRelating operations in time to filters in the frequency domainLinear time-invariant (LTI) system theoryAmplitude modulation (AM) used in radioOther books in this series include Think Stats and Think Bayes, also by Allen Downey.