Book picks similar to
A little book of R for time series by Avril Coghlan


ebook-not-read
machine-learning
mathematics
reference

Guerrilla Home Recording: How to Get Great Sound from Any Studio {No Matter How Weird or Cheap Your Gear Is}


Karl Coryat - 2004
    The revised edition is updated with a greater focus on digital recording techniques, the most powerful tools available to the home recordist. There are chapters devoted to instrument recording, humanizing drum patterns, mixing with plug-ins and virtual consoles, and a new section on using digital audio skills. And since, many true "Guerrillas" still record to analog tape, we have retained the best of that world. This edition features many more graphics than in the original edition, further enforcing Guerrilla Home Recording's reputation as the most readable, user-frienly recording title on the market.

Bayes Theorem: A Visual Introduction For Beginners


Dan Morris - 2016
    Bayesian statistics is taught in most first-year statistics classes across the nation, but there is one major problem that many students (and others who are interested in the theorem) face. The theorem is not intuitive for most people, and understanding how it works can be a challenge, especially because it is often taught without visual aids. In this guide, we unpack the various components of the theorem and provide a basic overview of how it works - and with illustrations to help. Three scenarios - the flu, breathalyzer tests, and peacekeeping - are used throughout the booklet to teach how problems involving Bayes Theorem can be approached and solved. Over 60 hand-drawn visuals are included throughout to help you work through each problem as you learn by example. The illustrations are simple, hand-drawn, and in black and white. For those interested, we have also included sections typically not found in other beginner guides to Bayes Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios.A few examples of how to think like a Bayesian in everyday life. Bayes Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. It is a great tool that can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes Rule. Bayes Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700’s to its being used to break the German’s Enigma Code during World War 2, its tale is quite phenomenal.Fascinating real-life stories on how Bayes formula is used in everyday life.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. We have summed up 3 examples for you and provided an example of how Bayes could be used.An expanded definitions, notations, and proof section.We have included an expanded definitions and notations sections at the end of the booklet. In this section we define core terms more concretely, and also cover additional terms you might be confused about. A recommended readings section.From The Theory That Would Not Die to a few other books, there are a number of recommendations we have for further reading. Take a look! If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Bayesian statistics is an incredibly fascinating topic and likely touches your life every single day. It is a very important tool that is used in data analysis throughout a wide-range of industries - so take an easy dive into the theorem for yourself with a visual approach!If you are looking for a short beginners guide packed with visual examples, this booklet is for you.

WordPress: The Missing Manual


Matthew MacDonald - 2012
    This jargon-free Missing Manual shows you how to use WordPress and its themes, plug-ins, and widgets to build just about any website you can imagine, from a classy blog to a basic e-commerce site.The important stuff you need to know:Create a blog. Get a free WordPress.com account, choose the right theme, and start publishing content.Build a website. Produce a professional-looking business site by customizing a WordPress theme.Add features. Choose from thousands of WordPress widgets and plug-ins to extend your site’s features.Mix in multimedia. Include slideshows, video clips, webcasts, podcasts, and music players.Involve your readers. Let readers leave comments, contribute to your site, and carry on a dialog.Build an audience. Learn search-engine optimization, measure your reader’s favorite pages, and publicize your site.Create a community. Use social media tools such as “Like” and sharing buttons, and provide RSS feeds of your posts.

Hands-On Machine Learning with Scikit-Learn and TensorFlow


Aurélien Géron - 2017
    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

Applied Multivariate Statistical Analysis


Richard A. Johnson - 1982
    of Wisconsin-Madison) and Wichern (Texas A&M U.) present the newest edition of this college text on the statistical methods for describing and analyzing multivariate data, designed for students who have taken two or more statistics courses. The fifth edition includes the addition of seve

Data Smart: Using Data Science to Transform Information into Insight


John W. Foreman - 2013
    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

Comptia A+ 220-801 and 220-802 Exam Cram


David L. Prowse - 2012
     Limited Time Offer: Buy CompTIA(R) A+ 220-801 and 220-802 Exam Cram and receive a 10% off discount code for the CompTIA A+ 220-801 and 220-802 exams. To receive your 10% off discount code:Register your product at pearsonITcertification.com/registerFollow the instructionsGo to your Account page and click on "Access Bonus Content" CompTIA(R) A+ 220-801 and 220-802 Exam Cram, Sixth Edition is the perfect study guide to help you pass CompTIA's A+ 220-801 and 220-802 exams. It provides coverage and practice questions for every exam topic, including substantial new coverage of Windows 7, new PC hardware, tablets, smartphones, and professional-level networking and security. The book presents you with an organized test preparation routine through the use of proven series elements and techniques. Exam topic lists make referencing easy. Exam Alerts, Sidebars, and Notes interspersed throughout the text keep you focused on what you need to know. Cram Quizzes help you assess your knowledge, and the Cram Sheet tear card is the perfect last minute review. Covers the critical information you'll need to know to score higher on your CompTIA A+ 220-801 and 220-802 exams!Deploy and administer desktops and notebooks running Windows 7, Vista, or XPUnderstand, install, and troubleshoot motherboards, processors, and memoryTest and troubleshoot power-related problemsUse all forms of storage, including new Blu-ray and Solid State (SSD) devicesWork effectively with mobile devices, including tablets and smartphonesInstall, configure, and troubleshoot both visible and internal laptop componentsConfigure Windows components and applications, use Windows administrative tools, and optimize Windows systemsRepair damaged Windows environments and boot errorsWork with audio and video subsystems, I/O devices, and the newest peripheralsInstall and manage both local and network printersConfigure IPv4 and understand TCP/IP protocols and IPv6 changesInstall and configure SOHO wired/wireless networks and troubleshoot connectivityImplement secure authentication, prevent malware attacks, and protect data Companion CDThe companion CD contains a digital edition of the Cram Sheet and the powerful Pearson IT Certification Practice Test engine, complete with hundreds of exam-realistic questions and two complete practice exams. The assessment engine offers you a wealth of customization options and reporting features, laying out a complete assessment of your knowledge to help you focus your study where it is needed most. Pearson IT Certifcation Practice Test Minimum System RequirementsWindows XP (SP3), WIndows Vista (SP2), or Windows 7Microsoft .NET Framework 4.0 ClientPentium-class 1 GHz processor (or equivalent)512 MB RAM650 MB disk space plus 50 MB for each downloaded practice exam David L. Prowse is an author, computer network specialist, and technical trainer. Over the past several years he has authored several titles for Pearson Education, including the well-received CompTIA A+ Exam Cram and CompTIA Security+ Cert Guide. As a consultant, he installs and secures the latest in computer and networking technology. He runs the website www.davidlprowse.com, where he gladly answers questions from students and readers.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction


Trevor Hastie - 2001
    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

The Dramatic Portrait: The Art of Crafting Light and Shadow


Chris Knight - 2017
    As almost every photographer knows, the word -photograph- has its roots in two Greek words that, together, mean -drawing with light.- But what is less commonly acknowledged and understood is the role that shadow plays in creating striking, expressive imagery, especially in portraiture. It is through deft, nuanced use of both light and shadow that you can move beyond shooting simply ordinary, competent headshots into the realm of creating dramatic portraiture that can so powerfully convey a subject's inner essence, communicate a personal narrative, and express your photographic vision. In The Dramatic Portrait: The Art of Crafting Light and Shadow, Chris Knight addresses portraiture with a unique approach to both light and shadow that allows you to improve and elevate your own portraiture. He begins with the history of portraiture, from the early work of Egyptians and Greeks to the sublime treatment of light and subject by artists such as Caravaggio, Rembrandt, and Vermeer. Chris then dives into a deep, hands-on exploration of light, shadow, and portraiture, offering numerous lessons and takeaways. He covers: - The qualities of light: hard, soft, and the spectrum in between- The relationships between light, subject, and background, and how to control them- Lighting patterns such as Paramount, Rembrandt, loop, and split- Lighting ratios and how they affect contrast in your image- Equipment: from big and small modifiers to grids, snoots, barn doors, flags, and gels- Multiple setups for portrait shoots, including those that utilize one, two, and three lights- How color contributes to drama and mood, eliciting an emotional response from the viewer- How to approach styling your portrait, from wardrobe to background- The post-processing workflow, including developing the RAW file, maximizing contrast, color grading, retouching, and dodging and burning for heightened drama and effect- How all of these elements culminate to help you define your personal style and create your own narrative

R for Dummies


Joris Meys - 2012
    R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset.Includes tips for the initial installation of RDemonstrates how to easily perform calculations on vectors, arrays, and lists of dataShows how to effectively visualize data using R's powerful graphics packagesGives pointers on how to find, install, and use add-on packages created by the R communityProvides tips on getting additional help from R mailing lists and websitesWhether you're just starting out with statistical analysis or are a procedural programming pro, "R For Dummies" is the book you need to get the most out of R.

Elementary Solid State Physics: Principles and Applications


M. Ali Omar - 1975
    I also hope that it will serve as a useful reference too for the many workers engaged in one type of solid state research activity or another, who may be without formal training in the subject.

Information Theory, Inference and Learning Algorithms


David J.C. MacKay - 2002
    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Mindfulness for everyday people: EVERYDAY MINDFULNESS IN PRACTICE: Simple and practical ways for everyday mindfulness


Anna Fox - 2017
    Whatever you do mindfully, with presence and awareness of what you are doing, you are practicing mindfulness. However, there are also some simple ways and techniques that you can apply in your life throughout the day that can help you with your mindfulness practice. I am not a life coach or psychologist, I am not some special person with some secret knowledge. I am just a regular everyday person and I spent a lot of years of my life seeking the answers on how to be my best self and live my best life, a mindful life. I read many books and took many courses to find those answers but most importantly I lived through a lot of experiences that made me who I am today and helped me grow, helped me become my best self. I struggled with depression, eating disorder, health problems, pain from losing my father at the age of 12, challenging and painful childhood and finding my purpose and meaning. I overcame a lot of pain, struggles, obstacles and setbacks and I can tell you from the experience that it can be done. Everything I share with you is my personal experience and practices that I tried and that worked for me. We will cover several morning, day and evening mindful practices: Mindful morning: - Mindful breathing - Mindful meditation - Sun Salutation Mindful day: - Mindful work - Mindful eating - Mindful communication Mindful evening: - Mindful walking - Gratitude - Body scanning Plus, I am offering a BONUS inside that you can download for FREE, that can help you with your mindfulness practice – “Gratitude Journal – 365 days of gratitude, inspiring quotes and photographs”. If you like simple and concrete advice that actually works in practice you will like this book. Download your copy today and start with your everyday mindfulness in practice!

Python Tricks: A Buffet of Awesome Python Features


Dan Bader - 2017
    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, Lego, and Rubber Ducks


Will Kurt - 2019
    But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that.This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:- How to measure your own level of uncertainty in a conclusion or belief- Calculate Bayes theorem and understand what it's useful for- Find the posterior, likelihood, and prior to check the accuracy of your conclusions- Calculate distributions to see the range of your data- Compare hypotheses and draw reliable conclusions from themNext time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.