Description : Right now, someone in an artificial intelligence lab is fusing silicon circuitry in an attempt to engineer the human mind. In a hospital, a neurosurgeon is attempting to influence a patient’s emotions by firing electrical impulses into his brain. In a classroom, a teacher is explaining how neurons in the brain interact to generate thoughts, feelings, and decisions. The question of where consciousness comes from and how it works is likely the greatest mystery we face. Despite progress in our knowledge of the brain, we still don’t know how it allows us to do things like enjoy a sunset, solve a math problem, or use our imagination. For those of us who have ever thought about issues of the mind or free will, these developments pose provocative questions. What would happen if those mysterious processes could be understood? Would a scientist be able to know everything about our minds just from studying the systems in our brains? Could he predict how we will think and act? After all, the brain is an organ just like the heart or stomach, and scientists can figure out when the heart will beat and when the stomach will release bile. If such a thing could be accomplished, would that make me a machine? There are those who approach this question from a technological perspective. Someday, an engineer might be able to build a robot with my memories, opinions, and behavior. Would that make me a machine? This concise, lucid primer on neuroscience and philosophy of mind takes the reader to the very depths of the mystery of consciousness, exploring it through the eyes of key philosophers, neuroscientists, and technologists. Avoiding jargon and oversimplification, author Eliezer J. Sternberg illuminates baffling questions of the brain, mind, and what it means to be human.
Description : A landmark volume that explores the interconnected nature of technologies and rhetorical practice Rhetorical Machines addresses new approaches to studying computational processes within the growing field of digital rhetoric. While computational code is often seen as value-neutral and mechanical, this volume explores the underlying, and often unexamined, modes of persuasion this code engages. In so doing, it argues that computation is in fact rife with the values of those who create it and thus has powerful ethical and moral implications. From Socrates’s critique of writing in Plato’s Phaedrus to emerging new media and internet culture, the scholars assembled here provide insight into how computation and rhetoric work together to produce social and cultural effects. This multidisciplinary volume features contributions from scholar-practitioners across the fields of rhetoric, computer science, and writing studies. It is divided into four main sections: “Emergent Machines” examines how technologies and algorithms are framed and entangled in rhetorical processes, “Operational Codes” explores how computational processes are used to achieve rhetorical ends, “Ethical Decisions and Moral Protocols” considers the ethical implications involved in designing software and that software’s impact on computational culture, and the final section includes two scholars’ responses to the preceding chapters. Three of the sections are prefaced by brief conversations with chatbots (autonomous computational agents) addressing some of the primary questions raised in each section. At the heart of these essays is a call for emerging and established scholars in a vast array of fields to reach interdisciplinary understandings of human-machine interactions. This innovative work will be valuable to scholars and students in a variety of disciplines, including but not limited to rhetoric, computer science, writing studies, and the digital humanities.
Description : Discover everything you need to build robust machine learning applications with Spark 2.0 About This Book Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2.0.0 Use Spark's machine learning library in a big data environment You will learn how to develop high-value applications at scale with ease and a develop a personalized design Who This Book Is For This book is for data science engineers and scientists who work with large and complex data sets. You should be familiar with the basics of machine learning concepts, statistics, and computational mathematics. Knowledge of Scala and Java is advisable. What You Will Learn Get solid theoretical understandings of ML algorithms Configure Spark on cluster and cloud infrastructure to develop applications using Scala, Java, Python, and R Scale up ML applications on large cluster or cloud infrastructures Use Spark ML and MLlib to develop ML pipelines with recommendation system, classification, regression, clustering, sentiment analysis, and dimensionality reduction Handle large texts for developing ML applications with strong focus on feature engineering Use Spark Streaming to develop ML applications for real-time streaming Tune ML models with cross-validation, hyperparameters tuning and train split Enhance ML models to make them adaptable for new data in dynamic and incremental environments In Detail Data processing, implementing related algorithms, tuning, scaling up and finally deploying are some crucial steps in the process of optimising any application. Spark is capable of handling large-scale batch and streaming data to figure out when to cache data in memory and processing them up to 100 times faster than Hadoop-based MapReduce. This means predictive analytics can be applied to streaming and batch to develop complete machine learning (ML) applications a lot quicker, making Spark an ideal candidate for large data-intensive applications. This book focuses on design engineering and scalable solutions using ML with Spark. First, you will learn how to install Spark with all new features from the latest Spark 2.0 release. Moving on, you'll explore important concepts such as advanced feature engineering with RDD and Datasets. After studying developing and deploying applications, you will see how to use external libraries with Spark. In summary, you will be able to develop complete and personalised ML applications from data collections,model building, tuning, and scaling up to deploying on a cluster or the cloud. Style and approach This book takes a practical approach where all the topics explained are demonstrated with the help of real-world use cases.
Description : Hannis Arc, working on the tapestry of lines linking constellations of elements that constituted the language of Creation recorded on the ancient Cerulean scroll spread out among the clutter on his desk, was not surprised to see the seven etherial forms billow into the room like acrid smoke driven on a breath of bitter breeze. Like an otherworldly collection of spectral shapes seemingly carried on random eddies of air, they wandered in a loose clutch among the still and silent mounted bears and beasts rising up on their stands, the small forest of stone pedestals holding massive books of recorded prophecy, and the evenly spaced display cases of oddities, their glass reflecting the firelight from the massive hearth at the side of the room. Since the seven rarely used doors, the shutters on the windows down on the ground level several stories below stood open as a fearless show of invitation. Though they frequently chose to use windows, they didn't actually need the windows any more than they needed the doors. They could seep through any opening, any crack, like vapor rising in the early morning from the stretches of stagnant water that lay in dark swaths through the peat barrens. The open shutters were meant to be a declaration for all to see, including the seven, that Hannis Arc feared nothing. In The Omen Machine, #1 New York Times-bestselling author Terry Goodkind returns to the lives of Richard Rahl and Kahlan Amnell—in a compelling tale of a new and sinister threat to their world. At the Publisher's request, this title is being sold without Digital Rights Management Software (DRM) applied.
Description : Ditch traditional corporate branding to create a powerful, recognizable brand Brand Against the Machine offers proven and actionable steps for companies and entrepreneurs to increase their brand visibility and credibility, and to create an indispensable brand that consumers can relate to, thus becoming life-long customers. Discover the aspirational currency that makes your brand one that people want to be or want to be friends with. Learn how to be real with your audience and make strategic associations to establish credibility. Brand Against the Machine will help you stand out, get noticed, and be remembered. Brand Against the Machine is the blueprint for how to market your brand to attract better clients and stand out from the clutter that is traditional corporate branding and marketing. Instant Positioning Method: How to instantly stand out from the crowd and position yourself as a resource, not just another service provider The 20/60/20 Rule: Why it's important to take a stand and why it's okay to have haters—because it creates a stronger bond with those who love you Ditch your traditional corporate branding and marketing, and exchange it for something memorable. Your customers will thank you for it.
Description : Dig deep into the data with a hands-on guide to machinelearning Machine Learning: Hands-On for Developers and TechnicalProfessionals provides hands-on instruction and fully-codedworking examples for the most common machine learning techniquesused by developers and technical professionals. The book contains abreakdown of each ML variant, explaining how it works and how it isused within certain industries, allowing readers to incorporate thepresented techniques into their own work as they follow along. Acore tenant of machine learning is a strong focus on datapreparation, and a full exploration of the various types oflearning algorithms illustrates how the proper tools can help anydeveloper extract information and insights from existing data. Thebook includes a full complement of Instructor's Materials tofacilitate use in the classroom, making this resource useful forstudents and as a professional reference. At its core, machine learning is a mathematical, algorithm-basedtechnology that forms the basis of historical data mining andmodern big data science. Scientific analysis of big data requires aworking knowledge of machine learning, which forms predictionsbased on known properties learned from training data. MachineLearning is an accessible, comprehensive guide for thenon-mathematician, providing clear guidance that allows readersto: Learn the languages of machine learning including Hadoop,Mahout, and Weka Understand decision trees, Bayesian networks, and artificialneural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficientmachine learning By learning to construct a system that can learn from data,readers can increase their utility across industries. Machinelearning sits at the core of deep dive data analysis andvisualization, which is increasingly in demand as companiesdiscover the goldmine hiding in their existing data. For the techprofessional involved in data science, Machine Learning:Hands-On for Developers and Technical Professionals providesthe skills and techniques required to dig deeper.
Description : The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.
Description : "Sheer pleasure. . . . Wonderfully entertaining."—Chicago Sun-Times Acclaimed by Norman Mailer more than twenty years ago as "possibly the only American writer of genius," William S. Burroughs has produced a body of work unique in our time. In these scintillating essays, he writes wittily and wisely about himself, his interests, his influences, his friends and foes. He offers candid and not always flattering assessments of such diverse writers as Ernest Hemingway, F. Scott Fitzgerald, Joseph Conrad, Graham Greene, Jack Kerouac, Allen Ginsberg, Samuel Beckett, and Marcel Proust. He ruminates on science and the often dubious paths into which it seems intent on leading us, whether into outer or inner space. He reviews his reviewers, explains his famous "cut-up" method, and discusses the role coincidence has played in his life and work. As satirist and parodist, William Burroughs has no peer, as these varied works, written over three decades, amply reveal.
Description : If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data
Description : This compact book explores standard tools for text classification, and teaches the reader how to use machine learning to decide whether a e-mail is spam or ham (binary classification), based on raw data from The SpamAssassin Public Corpus. Of course, sometimes the items in one class are not created equally, or we want to distinguish among them in some meaningful way. The second part of the book will look at how to not only filter spam from our email, but also placing "more important" messages at the top of the queue. This is a curated excerpt from the upcoming book "Machine Learning for Hackers."