PDF Modern Pattern Design Download
- Author: Harriet Pepin
- Publisher:
- ISBN:
- Category : Costume design
- Languages : en
- Pages :
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Apply modern C++17 to the implementations of classic design patterns. As well as covering traditional design patterns, this book fleshes out new patterns and approaches that will be useful to C++ developers. The author presents concepts as a fun investigation of how problems can be solved in different ways, along the way using varying degrees of technical sophistication and explaining different sorts of trade-offs. Design Patterns in Modern C++ also provides a technology demo for modern C++, showcasing how some of its latest features (e.g., coroutines) make difficult problems a lot easier to solve. The examples in this book are all suitable for putting into production, with only a few simplifications made in order to aid readability. What You Will Learn Apply design patterns to modern C++ programming Use creational patterns of builder, factories, prototype and singleton Implement structural patterns such as adapter, bridge, decorator, facade and more Work with the behavioral patterns such as chain of responsibility, command, iterator, mediator and more Apply functional design patterns such as Monad and more Who This Book Is For Those with at least some prior programming experience, especially in C++.
This practical guide explains how to take accurate measurements, introduces key tools and takes you from simple pattern-cutting ideas to more advanced creative methods. Step-by-step illustrations show how to create and then fit basic bodice, sleeve, skirt, dress, and trouser blocks, and how to adapt these to create patterns for original designs. New material includes advice on fitting toiles and working with stretch fabrics. There is also a fully updated chapter dedicated to digital technology. New to this edition: Access to 32 instructional videos
"Twentieth-Century Pattern Design combines photographs - including many newly published images - with soundly researched text, creating an essential resource for enthusiasts and historians of modern design. The book also serves as a creative sourcebook for students and designers, inspiring new flights of fancy in pattern design."--Jacket.
Scandinavian fashion has long been admired for its stylish, clean-lined, interesting cuts and aesthetic. Breaking the Pattern is the first authentic dressmaking book that showcases the beauty and uniqueness of Scandinavian style. Complete with stunning photography, clear illustrations and instructions this book will show you how to create a coherent capsule wardrobe – complete with a collection of 10 garments that are easy to make, wear, and combine with each other. From the Finnish sewing duo behind the dressmaking fashion label Named, Laura and Saara offer up an enticing selection of tops, dresses, skirt and trousers for the modern seamstress. You will build your skills as you work through the book and with plenty of variations suggested for each garment, there's lots of room for you to add your own personal touches and quirks to each design. Taking you beyond the patterns featured in this book, Breaking the Pattern will inspire you to not only build on your sewing repertoire but to grow in confidence with your own sewing. Patterns come in a range of sizes from US sizes 4 to 16, with concise information of measuring yourself and finding the perfect fit. Full size pattern sheets are included in this neat package, perfect for the contemporary dressmaker.
Covers all the basic steps of designing patterns including art manipulation and garment styling. Features include more advanced topics such as tailored collars and coats.
Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline