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Remembering Rachel
An account by Rose Calally of the tragic death of her daughter Rachel.
Remembering Rachel
The day Rose Callaly found her daughter Rachel's battered body was only the start of her nightmares. Shortly afterwards Rose became certain that the person who had killed her beautiful daughter was Rachel's husband, Joe O'Reilly. After what seemed like an eternity, O'Reilly was charged. But that was the start of another ordeal - the revelation of just how much he despised his wife and the unfolding of his ingenious plan to kill her, a plan that set Rose up to discover the murder scene. Remembering Rachel is the shocking and heart-breaking story of Rachel Callaly's short life and brutal death. It is also a remarkable account of what it is like to be at the heart of a sensational and tragic murder case. And finally, it is a touching portrait of motherly love and the bond that survives death.
Doing Data Science
Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an idea...
The Suspect
On a clear autumn morning in 2004 Rachel O’Reilly, a 30 year-old mother-of-two, was brutally battered to death in her home. It was a merciless killing that stunned the small, trusting community where she lived, and devastated her close-knit family. In the days that followed the discovery of her body, it was thought that Rachel was the victim of a bungled robbery attempt. It soon emerged, however, that police investigating the case believed Rachel had known her killer and that her murder had been carefully planned months in advance. The spotlight immediately fell upon Rachel’s husband, Joe O’Reilly, who admitted in a number of extraordinary press interviews that he was a prime suspect in his wife’s slaying. The 32-year-old advertising executive vehemently denied any involvement. It was a crime that captured the imagination of the public, who watched as the illusion of the idyllic suburban life the couple shared together began to shatter.
The Making of a Detective
When he retired in 2018 Pat Marry had been instrumental in solving dozens of serious crimes, including many murders. But as a newly qualified garda in 1985, Marry had no idea how to become a detective. He soon realised he would have to learn on the job - put himself forward and show that he had what it took. Taking initiative, following up hunches (even far-fetched ones), obsessing about details, trying new investigative techniques, thinking laterally - these were essential. In addition, you had to be a bit of a psychologist. The Making of a Detective follows Pat Marry's path from rookie to Detective Inspector through the stories of key cases he worked on and investigations he led. It includ...
High Performance Spark
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn ...
Natural Language Processing with PyTorch
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
BioBuilder
"[P]rovides open-access, modular, hands-on lessons in synthetic biology for secondary and post-secondary classrooms and laboratories"--Page [4] of book cover
The CSS Anthology
Provides information on Cascading Style Sheets, covering such topics as text styling, images, tabular data, forms and user interfaces, and positioning and layout.
Ethics and Data Science
As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
