To reach this unique audience, Whitlock and Schluter motivate learning with interesting biological and medical examples; they emphasize intuitive understanding; and they focus on real data. The book covers basic topics in introductory statistics, including graphs, confidence intervals, hypothesis testing, comparison of means, regression, and designing experiments. It also introduces the principles behind such modern topics as likelihood, linear models, meta-analysis and computer-intensive methods. Instructors and students consistently praise the book's clear and engaging writing, strong visualization techniques, and its variety of fascinating and relevant biological examples.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    Harry Gensler engages students with the basics of logic through practical examples and important arguments both in the history of philosophy and from contemporary philosophy. Using simple and manageable methods for testing arguments, students are led step-by-step to master the complexities of logic.The companion LogiCola instructional program and various teaching aids (including a teacher's manual) are available from the book's website: www.routledge.com/textbooks/gensler_l...

    Conflict between humans and machines is seen as inevitable and its outcome all too predictable.In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage.If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial.In a 2014 editorial co-authored with Stephen Hawking, Russell wrote, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." Solving the problem of control over AI is not just possible; it is the key that unlocks a future of unlimited promise.

    All chapters are supplemented by thought-provoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering. "Mathematicians wishing a self-contained introduction need look no further." — American Mathematical Monthly.

    This book can be cut, drawn in, folded into shapes and will even take you to the fourth dimension. So join stand-up mathematician Matt Parker on a journey through narcissistic numbers, optimal dating algorithms, at least two different kinds of infinity and more.

    What's new in the Second Edition? 1. Fully compliant with the latest version of SPSS version 12 2. More coverage of advanced statistics including completely new coverage of non-parametric statistics. The book is 50 per cent longer than the First Edition. 3. Each section of each chapter now has a notation - 1,2 or 3 - referring to the intended level of study. This helps students navigate their way through the book and makes it user-friendly for students of ALL levels. 4. Has a 'how to use this book' section at the start of the text. 5. Characters in each chapter have defined roles - summarizing key points, to pose questions etc 6. Each chapter now has several examples for students to work through. Answers provided on the enclosed CD-ROM

    Women and Sabarimala is a rare book and is written from a woman’s perspective, explaining the nature of the temple through India’s traditional knowledge systems, such as Ayurveda, Chakras, Tantra and Agama Shastra. At the same time, the author’s personal experiences simplify the understanding of these deep sciences, providing a glimpse into how temples impact the human physiology and, in particular, women’s menstrual cycles. This book will change the way Hindu temples, especially Sabarimala, are perceived and experienced.

    Although it has a long history, the recent discovery (1992) of the body's widespread endocannabinoid system (ECS) has thrust cannabis back into the limelight again as a viable medicine. In 2012 over 2.5 million prescriptions were written for medical marijuana in the United States. CBD and THC are both cannabinoids found in cannabis. However, while THC produces a “high” in the user, CBD does not. And now legal CBD-rich hemp oil is available over-the-counter in all 50 states, without a prescription. It was discovered that the Hemp Family of plants (cannabis, marijuana) is loaded with cannabinoids that stimulate our ECS receptor sites. These sites are found in the brain, organs, glands, connective tissue and immune cells and plays regulatory roles in many physiological processes including appetite, pain-sensation, mood and memory. The primary purpose of this system revolves around maintaining balance in the body. Cannabinoids found in all varieties of cannabis work in harmony with the cannabinoids we naturally produce when our system is functioning properly. It is now coming to light that we may very well be “Endocannabinoid Deficient” and supplementing with Cannabidiol, known as CBD, may provide just what we’re missing to restore optimum health. The health benefits of cannabidiol (CBD) from natural hemp oil is this book's primary focus. It explores the similarities, differences, uses and benefits of hemp, cannabis and medical marijuana along with the interplay of THC and CBD. Their 480 other components are also discussed, such as terpenoids, flavonoids, enzymes, vitamins, etc. Make no mistake about it, the endocannabinoid system, although newly discovered, is just as important as any other bodily system, like the muscular, cardiac, circulatory or digestive system. The ECS requires its own specialized medicine as found in the Hemp Family of plants, which is also known as cannabis, and which includes all strains of marijuana. Here is a list of conditions known and/or being researched that may be helped by cannabinoid therapeutics and supplementation: Acne ADD/ADHD Addiction AIDS ALS (Lou Gehrig's Disease) Alzheimer’s Anorexia Antibiotic Resistance Anxiety Atherosclerosis Arthritis Asthma Autism Bipolar Cancer Colitis/Crohn’s Depression Diabetes Endocrine Disorders Epilepsy/Seizure Fibromyalgia Glaucoma Heart Disease Huntington’s Inflammation Irritable Bowel Kidney Disease Liver Disease Metabolic Syndrome Migraine Mood Disorders Motion Sickness Multiple Sclerosis Nausea Neurodegeneration Neuropathic Pain Obesity OCD Osteoporosis Parkinson’s Prion/Mad Cow Disease PTSD Rheumatism Schizophrenia Sickle Cell Anemia Skin Conditions Sleep Disorders Spinal Cord Injury Stress Stroke/TBI 10% of the proceeds from sales of this book will be donated to the American Cannabis Nurses Association in an effort to bring together nurses, to share, integrate and one day certify nurses in the science of endocannabinoid therapeutics in nursing practice.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    is the most fun you'll ever have with graphs." -- Bustle Military intelligence analyst and Harvard Law student Tyler Vigen illustrates the golden rule that "correlation does not equal causation" through hilarious graphs inspired by his viral website.Is there a correlation between Nic Cage films and swimming pool accidents? What about beef consumption and people getting struck by lightning? Absolutely not. But that hasn't stopped millions of people from going to tylervigen.com and asking, "Wait, what?" Vigen has designed software that scours enormous data sets to find unlikely statistical correlations. He began pulling the funniest ones for his website and has since gained millions of views, hundreds of thousands of likes, and tons of media coverage. Subversive and clever, Spurious Correlations is geek humor at its finest, nailing our obsession with data and conspiracy theory.

    His Incompleteness Theorem turned not only mathematics but also the whole world of science and philosophy on its head. Equally legendary were Gö's eccentricities, his close friendship with Albert Einstein, and his paranoid fear of germs that eventually led to his death from self-starvation. Now, in the first popular biography of this strange and brilliant thinker, John Casti and Werner DePauli bring the legend to life. After describing his childhood in the Moravian capital of Brno, the authors trace the arc of Gö's remarkable career, from the famed Vienna Circle, where philosophers and scientists debated notions of truth, to the Institute for Advanced Study in Princeton, New Jersey, where he lived and worked until his death in 1978. In the process, they shed light on Gö's contributions to mathematics, philosophy, computer science, artificial intelligence -- even cosmology -- in an entertaining and accessible way.

    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.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.