Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning About This Book Proficiently analyze data and apply machine learning techniques Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R – it will help you increase your R expertise, regardless of your level of experience. What You Will Learn Get data into your R environment and prepare it for analysis Perform exploratory data analyses and generate meaningful visualizations of the data Generate various plots in R using the basic R plotting techniques Create presentations and learn the basics of creating apps in R for your audience Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm Build, tune, and evaluate predictive models with different machine learning packages Incorporate R and Hadoop to solve machine learning problems on big data In Detail The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan R Data Visualization Cookbook by Atmajitsinh Gohil Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu) Style and approach This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.
There is an ever increasing trend towards putting entire systems on a single chip. This means that analog circuits will have to coexist on the same substrate along with massive digital systems. Since technologies are optimized with these digital systems in mind, designers will have to make do with standard CMOS processes in the years to come. We address analog filter design from this perspective. Filters form important blocks in applications ranging from computer disc-drive chips to radio transceivers. In this book, we develop the theory and techniques necessary for the implementation of high frequency (hundreds of megahertz) programmable continuous time filters in standard CMOS processes. Since high density poly-poly capacitors are not available in these technologies, alternative capacitor structures have to be found. Met- metal capacitors have low specific capacitance. An alternative is to use the (inherently nonlinear) capacitance formed by MOSFET gates. In Chapter 2, we focus on the use of MOS capacitors as integrating elements. A physics-based model which predicts distortion accurately is presented for a two-terminal MOS structure in accumulation. Distortion in these capacitors as a function of signal swing and bias voltage is computed. Chapter 3 reviews continuous-time filter architectures in the light of bias-dependent integrating capacitors. We also discuss the merits and demerits of various CMOS transconductance elements. The problems encountered in designing high frequency programmable filters are discussed in detail.
This new edition introduces operation and design techniques for Sigma-Delta converters in physical and conceptual terms, and includes chapters which explore developments in the field over the last decade Includes information on MASH architectures, digital-to-analog converter (DAC) mismatch and mismatch shaping Investigates new topics including continuous-time ΔΣ analog-to-digital converters (ADCs) principles and designs, circuit design for both continuous-time and discrete-time ΔΣ ADCs, decimation and interpolation filters, and incremental ADCs Provides emphasis on practical design issues for industry professionals
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