Für einen erfolgreichen Hardware Entwurf sind nicht nur VHDL-Kenntnisse wichtig, sondern auch Kenntnisse der FPGA-Schaltungstechnik und der Design Tools. Das vorliegende Buch stellt die Zusammenhänge ...zwischen diesen wichtigen Themen dar und bietet eine zielgerichtete Einführung in den Entwurf von digitalen Schaltungen und Systemen mit FPGAs. Beginnend mit den Grundlagen von VHDL sowie der CMOS- und FPGA-Technologie, werden anschließend der synthesegerechte Entwurf mit VHDL und die synchrone Schaltungstechnik auf dem FPGA behandelt. Darüber hinaus werden auch die wesentlichen Entwurfswerkzeuge, wie Logiksynthese oder die statische Timing-Analyse, erläutert. Abgerundet wird das Buch mit einem Kapitel über High-Level Synthese, welche eine Umsetzung von C/C++-Code in eine VHDL-Implementierung ermöglicht. Der Leser erhält anhand vieler Code-Beispiele einen praxisorientierten Zugang zum Hardware-Entwurf mit FPGAs. Zielgerichtete Einführung in den digitalen Schaltungsentwurf Alle notwendigen Kenntnisse für den rechnergestützten Hardwareentwurf Frank Kesel studierte Elektrotechnik an der Universität Karlsruhe und promovierte an der Universität Hannover. Er war zehn Jahre in der Industrie im digitalen ASIC-Design tätig. Er ist seit 1999 Professor an der Hochschule Pforzheim mit dem Spezialgebiet FPGA-Design.
Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, ...and algorithms.
Relevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs. Gradually ...introducing applied statistics and the language and functionality of R and R Studio software, it uses examples from across the social sciences to show students how to apply abstract statistical and methodological principles to their own work. Maintaining a student-friendly pace, it goes beyond a normal introductory statistics book and shows students where data originates and how to: - Understand and use quantitative data to answer questions - Approach surrounding ethical issues - Collect quantitative data - Manage, write about, and share the data effectively Supported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives students not only the tools they need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what they have learned.
This book uses the Python language to teach programming concepts and problem-solving skills, without assuming any previous programming experience. With easy-to-understand examples, pseudocode, ...flowcharts, and other tools, the student learns how to design the logic of programs then implement those programs using Python. This book is ideal for an introductory programming course or a programming logic and design course using Python as the language.
A quality-driven design and verification flow for digital systems is developed and presented in Quality-Driven SystemC Design. Two major enhancements characterize the new flow: First, dedicated ...verification techniques are integrated which target the different levels of abstraction. Second, each verification technique is complemented by an approach to measure the achieved verification quality. The new flow distinguishes three levels of abstraction (namely system level, top level and block level) and can be incorporated in existing approaches. After reviewing the preliminary concepts, in the following chapters the three levels for modeling and verification are considered in detail. At each level the verification quality is measured. In summary, following the new design and verification flow a high overall quality results.
Currently employed at STMicroelectronics, Transactional-Level Modeling (TLM) puts forward a novel SoC design methodology beyond RTL with measured improvements of productivity and first time silicon ...success. The SystemC consortium has published the official TLM development kit in May 2005 to standardize this modeling technique. The library is flexible enough to model components and systems at many different levels of abstractions: from cycle-accurate to untimed models, and from bit-true behavior to floating-point algorithms. However, careful selection of the abstraction level and associated methodology is crucial to ensure practical gains for design teams. Transaction-Level Modeling with SystemC presents the formalized abstraction and related methodology defined at STMicroelectronics, and covers all major topics related to the Electronic System-Level (ESL) industry: - TLM modeling concepts - Early embedded software development based on SoC virtual prototypes - Functional verification using reference models - Architecture analysis with mixed TLM and cycle accurate platforms - Unifying TLM and RTL with platform automation tools Complementary to the book, open source code to put this approach into practice is available on several Internet sites as indicated in the first chapter.
Perform more advanced analysis and manipulation of your data beyond what Power BI can do to unlock valuable insights using Python and RKey FeaturesGet the most out of Python and R with Power BI by ...implementing non-trivial codeLeverage the toolset of Python and R chunks to inject scripts into your Power BI dashboardsImplement new techniques for ingesting, enriching, and visualizing data with Python and R in Power BIBook DescriptionPython and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages.
You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model.
By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.What you will learnDiscover best practices for using Python and R in Power BI productsUse Python and R to perform complex data manipulations in Power BIApply data anonymization and data pseudonymization in Power BILog data and load large datasets in Power BI using Python and REnrich your Power BI dashboards using external APIs and machine learning modelsExtract insights from your data using linear optimization and other algorithmsHandle outliers and missing values for multivariate and time-series dataCreate any visualization, as complex as you want, using R scriptsWho this book is forThis book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.
Get up and running with machine learning with F# in a fun and functional way About This Book • Design algorithms in F# to tackle complex computing problems • Be a proficient F# data scientist using ...this simple-to-follow guide • Solve real-world, data-related problems with robust statistical models, built for a range of datasets Who This Book Is For If you are a C# or an F# developer who now wants to explore the area of machine learning, then this book is for you. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage. What You Will Learn • Use F# to find patterns through raw data • Build a set of classification systems using Accord.NET, Weka, and F# • Run machine learning jobs on the Cloud with MBrace • Perform mathematical operations on matrices and vectors using Math.NET • Use a recommender system for your own problem domain • Identify tourist spots across the globe using inputs from the user with decision tree algorithms In Detail The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs. If you want to learn how to use F# to build machine learning systems, then this is the book you want. Starting with an introduction to the several categories on machine learning, you will quickly learn to implement time-tested, supervised learning algorithms. You will gradually move on to solving problems on predicting housing pricing using Regression Analysis. You will then learn to use Accord.NET to implement SVM techniques and clustering. You will also learn to build a recommender system for your e-commerce site from scratch. Finally, you will dive into advanced topics such as implementing neural network algorithms while performing sentiment analysis on your data. Style and approach This book is a fast-paced tutorial guide that uses hands-on examples to explain real-world applications of machine learning. Using practical examples, the book will explore several machine learning techniques and also describe how you can use F# to build machine learning systems.
