Final Draft Level 3 Student S Book

Final Draft combines academic writing skills, vocabulary, models, grammar, and a dedicated section on plagiarism.

Academic writing is difficult, and Final Draft gives students all the tools they need. Writing skills and in-depth analysis of models set the stage for development. Corpus-based vocabulary, collocations, and phrases, as well as detailed information on the grammar of writing, prepare your learners for college writing courses. Students learn to avoid plagiarism in every chapter of every level. This dedicated, long-term focus on plagiarism avoidance helps ensure that these students are able to use sources and highlight their own thoughts.

Academic writing is difficult, and Final Draft gives students all the tools they need. Writing skills and in-depth analysis of models set the stage for development. Corpus-based vocabulary, collocations, and phrases, as well as detailed information on the grammar of writing, prepare your learners for college writing courses. Students learn to avoid plagiarism in every chapter of every level. This dedicated, long-term focus on plagiarism avoidance helps ensure that these students are able to use sources and highlight their own thoughts.

Final Draft combines academic writing skills, vocabulary, models, grammar, and a dedicated section on plagiarism.

This book provides you with all the tools you need to write an excellent academic article and get it published.

′I find your straightforward writing style an absolute joy, such a breath of fresh air!′ - Angie Ash, PhD student ′...thank you very much for your accessible language, clear lay out and practical applied approach. I suspect that this book will never be far from my side over the next 4 years!!′ - Mayen Konarski, PhD student Using straight-forward language Doing Qualitative Research Using Your Computer walks readers through the process of managing and streamlining research projects using commonly available Microsoft software applications. Drawing on a wide range of examples to demonstrate how easy it is to use such software, this guide is full of useful hints and tips on how to manage research more efficiently and effectively, including: - Formatting transcripts for maximum coding efficiency in Microsoft Word - Using features of Word to organize the analysis of data and to facilitate efficient qualitative coding - Synchronizing codes, categories, and important concepts between Microsoft Word and Microsoft Access - Efficiently storing and analyzing the qualitative data in Microsoft Excel - Creating flexible analytic memos in Access that help lead the researcher to final conclusions Ideal for those students or researchers who don′t want to invest in expensive specialised software packages, this guide will be an invaluable companion for anyone embarking on their own research project.

Consumption of alcohol: Illegal. Football and other "violent" sports: Illegal. Ownership of guns, chain saws, and/or large dogs: Illegal. Body piercings, tattoos: Illegal. It's late in the twenty-first century, and the United Safer States of America (USSA) has become a nation obsessed with safety. For Bo Marsten, a teenager who grew up in the USSA, it's all good. He knows the harsh laws were created to protect the people. But when Bo's temper flares out of control and he's sentenced to three years of manual labor, he's not so down with the law anymore. Bo's forced to live and work in a factory in the Canadian tundra. The warden running the place is totally out of his mind, and cares little for his inmates' safety. Bo will have to decide what's worse: a society that locks people up for road rage, or a prison where the wrong move could make you polar bear food.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.