Download Free First Editions Press Books Book in PDF and EPUB Free Download. You can read online First Editions Press Books and write the review.

Tell kids not to worry. sorting my life out. be in touch to get some things. Instead of being a simple sms message, this text turned out to be crucial and chilling evidence in convicting the deceptive killer of a mother of two. Sent from her phone, after her death, tell tale signs announce themselves to a forensic linguist. Rarely is a crime committed without there being some evidence in the form of language. Wordcrime features a series of chapters where gripping cases are described - involving murder, sexual assault, hate mail, suspicious death, code deciphering, arson and even genocide. Olsson describes the evidence he gave in each one. In approachable and clear prose, he details how forensic linguistics helps the law beat the criminals. This is fascinating reading for anyone interested in true crime, in modern, cutting-edge criminology and also where the study of language meets the law.
Now an Academy Award-winning Netflix film by Jane Campion, starring Benedict Cumberbatch and Kirsten Dunst: Thomas Savage's acclaimed Western is "a pitch-perfect evocation of time and place" (Boston Globe) for fans of East of Eden and Brokeback Mountain. Set in the wide-open spaces of the American West, The Power of the Dog is a stunning story of domestic tyranny, brutal masculinity, and thrilling defiance from one of the most powerful and distinctive voices in American literature. The novel tells the story of two brothers — one magnetic but cruel, the other gentle and quiet — and of the mother and son whose arrival on the brothers’ ranch shatters an already tenuous peace. From the novel’s startling first paragraph to its very last word, Thomas Savage’s voice — and the intense passion of his characters — holds readers in thrall. "Gripping and powerful...A work of literary art." —Annie Proulx, from her afterword
The steadfast and sturdy Continental Op has been summoned to the town of Personville—known as Poisonville—a dusty mining community splintered by competing factions of gangsters and petty criminals. The Op has been hired by Donald Willsson, publisher of the local newspaper, who gave little indication about the reason for the visit. No sooner does the Op arrive, than the body count begins to climb . . . starting with his client. With this last honest citizen of Poisonville murdered, the Op decides to stay on and force a reckoning—even if that means taking on an entire town. Red Harvest is more than a superb crime novel: it is a classic exploration of corruption and violence in the American grain.
DigiCat Publishing presents to you this special edition of "The Long Goodbye" by Raymond Chandler. DigiCat Publishing considers every written word to be a legacy of humankind. Every DigiCat book has been carefully reproduced for republishing in a new modern format. The books are available in print, as well as ebooks. DigiCat hopes you will treat this work with the acknowledgment and passion it deserves as a classic of world literature.
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
The New York Times bestselling work of undercover reportage from our sharpest and most original social critic, with a new foreword by Matthew Desmond, author of Evicted Millions of Americans work full time, year round, for poverty-level wages. In 1998, Barbara Ehrenreich decided to join them. She was inspired in part by the rhetoric surrounding welfare reform, which promised that a job—any job—can be the ticket to a better life. But how does anyone survive, let alone prosper, on $6 an hour? To find out, Ehrenreich left her home, took the cheapest lodgings she could find, and accepted whatever jobs she was offered. Moving from Florida to Maine to Minnesota, she worked as a waitress, a hotel maid, a cleaning woman, a nursing-home aide, and a Wal-Mart sales clerk. She lived in trailer parks and crumbling residential motels. Very quickly, she discovered that no job is truly "unskilled," that even the lowliest occupations require exhausting mental and muscular effort. She also learned that one job is not enough; you need at least two if you int to live indoors. Nickel and Dimed reveals low-rent America in all its tenacity, anxiety, and surprising generosity—a land of Big Boxes, fast food, and a thousand desperate stratagems for survival. Read it for the smoldering clarity of Ehrenreich's perspective and for a rare view of how "prosperity" looks from the bottom. And now, in a new foreword, Matthew Desmond, author of Evicted: Poverty and Profit in the American City, explains why, twenty years on in America, Nickel and Dimed is more relevant than ever.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Optical coherence tomography (OCT) is the optical analog of ultrasound imaging and is emerging as a powerful imaging technique that enables non-invasive, in vivo, high resolution, cross-sectional imaging in biological tissue. This book introduces OCT technology and applications not only from an optical and technological viewpoint, but also from biomedical and clinical perspectives. The chapters are written by leading research groups, in a style comprehensible to a broad audience.
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.