Book picks similar to
Algorithms for Reinforcement Learning by Csaba Szepesvari
machine-learning
data-science
textbooks
technology
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists
Philipp K. Janert - 2010
With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora
Murach's HTML5 and CSS3: Training and Reference
Zak Ruvalcaba - 2011
This title also teaches you how to use the HTML5 and CSS3 features alongside the earlier standards.
Programming in Scala
Martin Odersky - 2008
Coauthored by the designer of the Scala language, this authoritative book will teach you, one step at a time, the Scala language and the ideas behind it. The book is carefully crafted to help you learn. The first few chapters will give you enough of the basics that you can already start using Scala for simple tasks. The entire book is organized so that each new concept builds on concepts that came before - a series of steps that promises to help you master the Scala language and the important ideas about programming that Scala embodies. A comprehensive tutorial and reference for Scala, this book covers the entire language and important libraries.
Understanding Digital Signal Processing
Richard G. Lyons - 1996
This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.
Systems Analysis and Design
Alan Dennis - 2002
Building on their experience as professional systems analysts and award-winning teachers, authors Dennis, Wixom, and Roth capture the experience of developing and analyzing systems in a way that students can understand and apply.With
Systems Analysis and Design, 4th edition
, students will leave the course with experience that is a rich foundation for further work as a systems analyst.
Handbook of Applied Cryptography
Alfred J. Menezes - 1996
Standards are emerging to meet the demands for cryptographic protection in most areas of data communications. Public-key cryptographic techniques are now in widespread use, especially in the financial services industry, in the public sector, and by individuals for their personal privacy, such as in electronic mail. This Handbook will serve as a valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography. It is a necessary and timely guide for professionals who practice the art of cryptography. The Handbook of Applied Cryptography provides a treatment that is multifunctional: It serves as an introduction to the more practical aspects of both conventional and public-key cryptographyIt is a valuable source of the latest techniques and algorithms for the serious practitionerIt provides an integrated treatment of the field, while still presenting each major topic as a self-contained unitIt provides a mathematical treatment to accompany practical discussionsIt contains enough abstraction to be a valuable reference for theoreticians while containing enough detail to actually allow implementation of the algorithms discussedNow in its third printing, this is the definitive cryptography reference that the novice as well as experienced developers, designers, researchers, engineers, computer scientists, and mathematicians alike will use.
Doing Data Science
Cathy O'Neil - 2013
But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS
John K. Kruschke - 2010
Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884 0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke
Real World OCaml: Functional programming for the masses
Yaron Minsky - 2013
Through the book’s many examples, you’ll quickly learn how OCaml stands out as a tool for writing fast, succinct, and readable systems code.Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. In the book’s third section, you’ll delve deep into the details of the compiler toolchain and OCaml’s simple and efficient runtime system.Learn the foundations of the language, such as higher-order functions, algebraic data types, and modulesExplore advanced features such as functors, first-class modules, and objectsLeverage Core, a comprehensive general-purpose standard library for OCamlDesign effective and reusable libraries, making the most of OCaml’s approach to abstraction and modularityTackle practical programming problems from command-line parsing to asynchronous network programmingExamine profiling and interactive debugging techniques with tools such as GNU gdb
Network Security: Private Communication in a Public World
Charlie Kaufman - 1995
In the second edition of Network Security, this most distinguished of author teams draws on hard-won experience to explain every facet of information security, from the basics to advanced cryptography and authentication; secure Web and email services; and emerging security standards. Highlights of the book's extensive new coverage include Advanced Encryption Standard (AES), IPsec, SSL, PKI Standards, and Web security.
C# 5.0 in a Nutshell: The Definitive Reference
Joseph Albahari - 2012
Uniquely organized around concepts and use cases, this updated fifth edition features a reorganized section on concurrency, threading, and parallel programming—including in-depth coverage of C# 5.0’s new asynchronous functions.Shaped by more than 20 expert reviewers, including Microsoft’s Eric Lippert, Stephen Toub, Chris Burrows, and Jon Skeet, this book has all you need to stay on track with C# 5.0. It’s widely known as the definitive reference on the language.Get up to speed on C# language basics, including syntax, types, and variablesExplore advanced topics such as unsafe code and type varianceDig deep into LINQ via three chapters dedicated to the topicLearn about code contracts, dynamic programming, and parallel programmingWork with .NET features, including reflection, assemblies, memory management, security, I/O, XML, collections, networking, and native interoperability"C# 5.0 in a Nutshell is one of the few books I keep on my desk as a quick reference." —Scott Guthrie, Microsoft"Whether you’re a novice programmer or an expert who wants to improve your knowledge of modern asynchronous programming techniques, this book has the information you need to get the job done in C#." —Eric Lippert, Microsoft
How to Create a Mind: The Secret of Human Thought Revealed
Ray Kurzweil - 2012
In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications of vastly increasing the powers of our intelligence in addressing the world’s problems. He thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating.Certain to be one of the most widely discussed and debated science books of the year, How to Create a Mind is sure to take its place alongside Kurzweil’s previous classics which include Fantastic Voyage: Live Long Enough to Live Forever and The Age of Spiritual Machines.
The Art of Statistics: How to Learn from Data
David Spiegelhalter - 2019
Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.
Thinking in C++
Bruce Eckel - 1995
It shows readers how to step back from coding to consider design strategies and attempt to get into the head of the designer.
Machine Learning With Random Forests And Decision Trees: A Mostly Intuitive Guide, But Also Some Python
Scott Hartshorn - 2016
They are typically used to categorize something based on other data that you have. The purpose of this book is to help you understand how Random Forests work, as well as the different options that you have when using them to analyze a problem. Additionally, since Decision Trees are a fundamental part of Random Forests, this book explains how they work. This book is focused on understanding Random Forests at the conceptual level. Knowing how they work, why they work the way that they do, and what options are available to improve results. This book covers how Random Forests work in an intuitive way, and also explains the equations behind many of the functions, but it only has a small amount of actual code (in python). This book is focused on giving examples and providing analogies for the most fundamental aspects of how random forests and decision trees work. The reason is that those are easy to understand and they stick with you. There are also some really interesting aspects of random forests, such as information gain, feature importances, or out of bag error, that simply cannot be well covered without diving into the equations of how they work. For those the focus is providing the information in a straight forward and easy to understand way.